Dissertations / Theses: 'Relevance feedback'

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Zhang, Peng. "Approximating true relevance model in relevance feedback." Thesis, Robert Gordon University, 2013. http://hdl.handle.net/10059/808.

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Relevance is an essential concept in information retrieval (IR) and relevance estimation is a fundamental IR task. It involves not only document relevance estimation, but also estimation of user's information need. Relevance-based language model aims to estimate a relevance model (i.e., a relevant query term distribution) from relevance feedback documents. The true relevance model should be generated from truly relevant documents. The ideal estimation of the true relevance model is expected to be not only effective in terms of mean retrieval performance (e.g., Mean Average Precision) over all the queries, but also stable in the sense that the performance is stable across different individual queries. In practice, however, in approximating/estimating the true relevance model, the improvement of retrieval effectiveness often sacrifices the retrieval stability, and vice versa. In this thesis, we propose to explore and analyze such effectiveness-stability tradeoff from a new perspective, i.e., the bias-variance tradeoff that is a fundamental theory in statistical estimation. We first formulate the bias, variance and the trade-off between them for retrieval performance as well as for query model estimation. We then analytically and empirically study a number of factors (e.g., query model complexity, query model combination, document weight smoothness and irrelevant documents removal) that can affect the bias and variance. Our study shows that the proposed bias-variance trade-off analysis can serve as an analytical framework for query model estimation. We then investigate in depth on two particular key factors: document weight smoothness and removal of irrelevant documents, in query model estimation, by proposing novel methods for document weight smoothing and irrelevance distribution separation, respectively. Systematic experimental evaluation on TREC collections shows that the proposed methods can improve both retrieval effectiveness and retrieval stability of query model estimation. In addition to the above main contributions, we also carry out initial exploration on two further directions: the formulation of bias-variance in personalization and looking at the query model estimation via a novel theoretical angle (i.e., Quantum theory) that has partially inspired our research.

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Xu, Zuobing. "Active relevance feedback algorithms /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2008. http://uclibs.org/PID/11984.

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Yamout, Fadi. "Relevance Feedback Using Weight Propagation." Thesis, University of Sunderland, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485442.

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A new relevance feedback technique called Weight Propagation has been developed which provides greater retrieval effectiveness than previously described techniques. Documents judged relevant by the user propagate positive Weights to documents close by in vector similarity space, while documents judged not relevant propagate Negative Weights to such neighbouring documents. Variants of Weight Propagation are also described, namely WPI and WPR (inspired by Ide and Rocchio respectively), and WPY which is the main focus of this thesis, where only the maximum weight propagated to each document is counted. Weight Propagation was further enhanced by introducing a second-order propagation (documents that receive weights themselve!propagate weights to related documents) which increased the precision of the results. WPY is compared against the Rocchio and Ide techniques in the vector model based on the tf.idf weighting scheme, and against the Information-theoretic query expansion technique based on the Kullback-Leibler divergence measure using the DB2 weighting model of the Divergence From Randomness framework. Different RF models were employed such as pseudo relevance feedback in addition to both simulated positive and negative feedback using residual collection technique. The experiments are performed on different test collections such as MED, CISI, Cranfield, LISA, NPL, WTIOG and GOV. Small collections such as MED, CISI, and Cranfield were also tested in the semantic space using Latent Semantic Indexing and the optimal number of dimensions that captures the underlying semantics that exists between the documents is determined for these collections. Retrieval effectiveness is improved since the documents are treated as independent vectors rather than being merged into a single vector as is the case with traditional vector model relevance feedback techniques, or by determining the documents' relevancy based on the lengths of all the documents as with the Kullback-Leibler divergence measure used in traditional probabilistic relevance feedback techniques. In addition, the Weight Propagation technique does not expand terms as in the case with traditional approaches.

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Ruthven, Ian. "Abduction, explanation and relevance feedback." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392605.

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Krishnan, Sharenya. "Text-Based Information Retrieval Using Relevance Feedback." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-53603.

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Europeana, a freely accessible digital library with an idea to make Europe's cultural and scientific heritage available to the public was founded by the European Commission in 2008. The goal was to deliver a semantically enriched digital content with multilingual access to it. Even though they managed to increase the content of data they slowly faced the problem of retrieving information in an unstructured form. So to complement the Europeana portal services, ASSETS (Advanced Search Service and Enhanced Technological Solutions) was introduced with services that sought to improve the usability and accessibility of Europeana. My contribution is to study different text-based information retrieval models, their relevance feedback techniques and to implement one simple model. The thesis explains a detailed overview of the information retrieval process along with the implementation of the chosen strategy for relevance feedback that generates automatic query expansion. Finally, the thesis concludes with the analysis made using relevance feedback, discussion on the model implemented and then an assessment on future use of this model both as a continuation of my work and using this model in ASSETS.

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Vinay, V. "The relevance of feedback for text retrieval." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1446146/.

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Relevance Feedback is a technique that helps an Information Retrieval system modify a query in response to relevance judgements provided by the user about individual results dis played after an initial retrieval. This thesis begins by proposing an evaluation framework for measuring the effectiveness of feedback algorithms. The simulation-based method in volves a brute force exploration of the outcome of every possible user action. Starting from an initial state, each available alternative is represented as a traversal along one branch of a user decision tree. The use of the framework is illustrated in two situations---searching on devices with small displays and for web search. Three well known RF algorithms, Rocchio, Robertson/Sparck-Jones (RSJ) and Bayesian, are compared for these applications. For small display devices, the algorithms are evaluated in conjunction with two strate gies for presenting search results: the top-D ranked documents and a document ranking that attempts to maximise information gain from the user's choices. Experimental results in dicate that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For the other two algorithms, the exploratory display that maximises information gain produces better results. A user study was conducted to evaluate the performance of the relevance feedback methods with real users and compare the results with the findings from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm, coupled with the sampled display, is the most effective. For web-search, two possible representations for web-pages are considered---the textual content of the page and the anchor text of hyperlinks into this page. Results indicate that there is a significant variation in the upper-bound performance of the three RF algorithms and that the Bayesian algorithm approaches the best possible. The relative performance of the three algorithms differed in the two sets of experiments. All other factors being constant, this difference in effectiveness was attributed to the fact that the datasets used in the two cases were different. Also, at a more general level, a relationship was observed between the performance of the original query and benefits of subsequent relevance feedback. The remainder of the thesis looks at properties that characterise sets of documents with the particular aim of identifying measures that are predictive of future performance of statis tical algorithms on these document sets. The central hypothesis is that a set of points (corresponding to documents) are difficult if they lack structure. Three properties are identified---the clustering tendency, sensitivity to perturbation and the local intrinsic dimensionality. The clustering tendency reflects the presence or absence of natural groupings within the data. Perturbation analysis looks at the sensitivity of the similarity metric to small changes in the input. The correlation present in sets of points is measured by the local intrinsic dimensionality therefore indicating the randomness present in them. These properties are shown to be useful for two tasks, namely, measuring the complexity of text datasets and for query performance prediction.

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Gusenbauer, Stefan. "Relevance feedback in information retrieval a comparison including a practical evaluation of several approaches to relevance feedback in information retrieval." Saarbrücken VDM Verlag Dr. Müller, 2006. http://d-nb.info/989497259/04.

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Jin, Lan. "Relevance feedback in the retrieval of reusable software components." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22831.pdf.

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Djordjevic, Divna. "User relevance feedback, search and retrieval of visual content." Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432897.

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Ng, Chee Un. "Image retrieval using experience-based relevance feedback and visualisation." Thesis, University of Warwick, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425556.

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Cheng, Sijin. "Relevance feedback-based optimization of search queries for Patents." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154173.

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In this project, we design a search query optimization system based on the user’s relevance feedback by generating customized query strings for existing patent alerts. Firstly, the Rocchio algorithm is used to generate a search string by analyzing the characteristics of related patents and unrelated patents. Then the collaborative filtering recommendation algorithm is used to rank the query results, which considering the previous relevance feedback and patent features, instead of only considering the similarity between query and patents as the traditional method. In order to further explore the performance of the optimization system, we design and conduct a series of evaluation experiments regarding TF-IDF as a baseline method. Experiments show that, with the use of generated search strings, the proportion of unrelated patents in search results is significantly reduced over time. In 4 months, the precision of the retrieved results is optimized from 53.5% to 72%. What’s more, the rank performance of the method we proposed is better than the baseline method. In terms of precision, top10 of recommendation algorithm is about 5 percentage points higher than the baseline method, and top20 is about 7.5% higher. It can be concluded that the approach we proposed can effectively optimize patent search results by learning relevance feedback.

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Monti, Eleonora. "Recupero con "relevance feedback" di immagini dermatologiche mediante funzioni filtranti." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14686/.

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L'incremento dei casi di melanoma cutaneo e la complessità della diagnosi portano spesso gli specialisti a ricorrere al supporto di macchinari per la diagnosi strumentale. A tal proposito è stato realizzato un progetto per la costruzione di una macchina ad alta tecnologia per il supporto medico nell'individuazione di lesioni melanocitiche. La macchina contiene un database di immagini cliniche di nevi e melanomi e, una volta acquisita l' immagine di una nuova lesione, la confronta con tutte quelle del database e recupera le immagini più vicine a quella da esaminare. Il problema affrontato in questa tesi è il seguente: spesso le immagini recuperate dalla macchina come le più "vicine" a quella in esame, vengono giudicate dai medici con valutazioni piuttosto basse di somiglianza; pertanto il lavoro svolto mira a migliorare ulteriormente la ricerca delle immagini dal database tenendo conto, in fase di recupero, dei pareri forniti dai medici (relevance feedback). In particolare sono state implementate due diverse tecniche di relevance feedback, una basata sui massimi e una sulle somme pesate, che sono state testate su delle immagini campione provenienti da due diversi database con risultati piuttosto soddisfacenti.

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Efthimiadis, Efthimis Nikolaos. "Interactive query expansion and relevance feedback for document retrieval systems." Thesis, City University London, 1992. http://openaccess.city.ac.uk/7891/.

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This thesis is aimed at investigating interactive query expansion within the context of a relevance feedback system that uses term weighting and ranking in searching online databases that are available through online vendors. Previous evaluations of relevance feedback systems have been made in laboratory conditions and not in a real operational environment. The research presented in this thesis followed the idea of testing probabilistic retrieval techniques in an operational environment. The overall aim of this research was to investigate the process of interactive query expansion (IQE) from various points of view including effectiveness. The INSPEC database, on both Data-Star and ESA-IRS, was searched online using CIRT, a front-end system that allows probabilistic term weighting, ranking and relevance feedback. The thesis is divided into three parts. Part I of the thesis covers background information and appropriate literature reviews with special emphasis on the relevance weighting theory (Binary Independence Model), the approaches to automatic and semi-automatic query expansion, the ZOOM facility of ESA/IRS and the CIRT front-end. Part II is comprised of three Pilot case studies. It introduces the idea of interactive query expansion and places it within the context of the weighted environment of CIRT. Each Pilot study looked at different aspects of the query expansion process by using a front-end. The Pilot studies were used to answer methodological questions and also research questions about the query expansion terms. The knowledge and experience that was gained from the Pilots was then applied to the methodology of the study proper (Part III). Part III discusses the Experiment and the evaluation of the six ranking algorithms. The Experiment was conducted under real operational conditions using a real system, real requests, and real interaction. Emphasis was placed on the characteristics of the interaction, especially on the selection of terms for query expansion. Data were collected from 25 searches. The data collection mechanisms included questionnaires, transaction logs, and relevance evaluations. The results of the Experiment are presented according to their treatment of query expansion as main results and other findings in Chapter 10. The main results discuss issues that relate directly to query expansion, retrieval effectiveness, the correspondence of the online-to-offline relevance judgements, and the performance of the w(p — q) ranking algorithm. Finally, a comparative evaluation of six ranking algorithms was performed. The yardstick for the evaluation was provided by the user relevance judgements on the lists of the candidate terms for query expansion. The evaluation focused on whether there are any similarities in the performance of the algorithms and how those algorithms with similar performance treat terms. This abstract refers only to the main conclusions drawn from the results of the Experiment: (1) One third of the terms presented in the list of candidate terms was on average identified by the users as potentially useful for query expansion; (2) These terms were mainly judged as either variant expression (synonyms) or alternative (related) terms to the initial query terms. However, a substantial portion of the selected terms were identified as representing new ideas. (3) The relationship of the 5 best terms chosen by the users for query expansion to the initial query terms was: (a) 34% have no relationship or other type of correspondence with a query term; (b) 66% of the query expansion terms have a relationship which makes the term: (bl) narrower term (70%), (b2) broader term (5%), (b3) related term (25%). (4) The results provide some evidence for the effectiveness of interactive query expansion. The initial search produced on average 3 highly relevant documents at a precision of 34%; the query expansion search produced on average 9 further highly relevant documents at slightly higher precision. (5) The results demonstrated the effectiveness of the w(p—q) algorithm, for the ranking of terms for query expansion, within the context of the Experiment. (6) The main results of the comparative evaluation of the six ranking algorithms, i.e. w(p — q), EMIM, F4, F4modifed, Porter and ZOOM, are that: (a) w(p — q) and EMIM performed best; and (b) the performance between w(p — q) and EMIM and between F4 and F4modified is very similar; (7) A new ranking algorithm is proposed as the result of the evaluation of the six algorithms. Finally, an investigation is by definition an exploratory study which generates hypotheses for future research. Recommendations and proposals for future research are given. The conclusions highlight the need for more research on weighted systems in operational environments, for a comparative evaluation of automatic vs interactive query expansion, and for user studies in searching weighted systems.

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Man, Chun Him. "Human face image searching system with relevance feedback using sketch." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/618.

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Liu, Danzhou. "EFFICIENT TECHNIQUES FOR RELEVANCE FEEDBACK PROCESSING IN CONTENT-BASED IMAGE RETRIEVAL." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2991.

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In content-based image retrieval (CBIR) systems, there are two general types of search: target search and category search. Unlike queries in traditional database systems, users in most cases cannot specify an ideal query to retrieve the desired results for either target search or category search in multimedia database systems, and have to rely on iterative feedback to refine their query. Efficient evaluation of such iterative queries can be a challenge, especially when the multimedia database contains a large number of entries, and the search needs many iterations, and when the underlying distance measure is computationally expensive. The overall processing costs, including CPU and disk I/O, are further emphasized if there are numerous concurrent accesses. To address these limitations involved in relevance feedback processing, we propose a generic framework, including a query model, index structures, and query optimization techniques. Specifically, this thesis has five main contributions as follows. The first contribution is an efficient target search technique. We propose four target search methods: naive random scan (NRS), local neighboring movement (LNM), neighboring divide-and-conquer (NDC), and global divide-and-conquer (GDC) methods. All these methods are built around a common strategy: they do not retrieve checked images (i.e., shrink the search space). Furthermore, NDC and GDC exploit Voronoi diagrams to aggressively prune the search space and move towards target images. We theoretically and experimentally prove that the convergence speeds of GDC and NDC are much faster than those of NRS and recent methods. The second contribution is a method to reduce the number of expensive distance computation when answering k-NN queries with non-metric distance measures. We propose an efficient distance mapping function that transfers non-metric measures into metric, and still preserves the original distance orderings. Then existing metric index structures (e.g., M-tree) can be used to reduce the computational cost by exploiting the triangular inequality property. The third contribution is an incremental query processing technique for Support Vector Machines (SVMs). SVMs have been widely used in multimedia retrieval to learn a concept in order to find the best matches. SVMs, however, suffer from the scalability problem associated with larger database sizes. To address this limitation, we propose an efficient query evaluation technique by employing incremental update. The proposed technique also takes advantage of a tuned index structure to efficiently prune irrelevant data. As a result, only a small portion of the data set needs to be accessed for query processing. This index structure also provides an inexpensive means to process the set of candidates to evaluate the final query result. This technique can work with different kernel functions and kernel parameters. The fourth contribution is a method to avoid local optimum traps. Existing CBIR systems, designed around query refinement based on relevance feedback, suffer from local optimum traps that may severely impair the overall retrieval performance. We therefore propose a simulated annealing-based approach to address this important issue. When a stuck-at-a-local-optimum occurs, we employ a neighborhood search technique (i.e., simulated annealing) to continue the search for additional matching images, thus escaping from the local optimum. We also propose an index structure to speed up such neighborhood search. Finally, the fifth contribution is a generic framework to support concurrent accesses. We develop new storage and query processing techniques to exploit sequential access and leverage inter-query concurrency to share computation. Our experimental results, based on the Corel dataset, indicate that the proposed optimization can significantly reduce average response time while achieving better precision and recall, and is scalable to support a large user community. This latter performance characteristic is largely neglected in existing systems making them less suitable for large-scale deployment. With the growing interest in Internet-scale image search applications, our framework offers an effective solution to the scalability problem.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD

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Wong, Chan Fong. "Content-based image retrieval using color quantization, rectangular segmentation, and relevance feedback." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1780398.

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Ouattara, Anatole Kinaya. "La recherche d'informations sur le Web et la méthode du Relevance Feedback." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0007/MQ41973.pdf.

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Menard, Jr Kevin Joseph. "Evaluating User Feedback Systems." Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-theses/702.

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The increasing reliance of people on computers for daily tasks has resulted in a vast number of digital documents. Search engines were once luxury tools for quickly scanning a set of documents but are now quickly becoming the only practical way to navigate through this sea of information. Traditionally, search engine results are based upon a mathematical formula of document relevance to a search phrase. Often, however, what a user deems to be relevant and what a search engine computes as relevant are not the same. User feedback regarding the utility of a search result can be collected in order to refine query results. Additionally, user feedback can be used to identify queries that lack high quality search results. A content author can then further develop existing content or create new content to improve those search results. The most straightforward way of collecting user feedback is to add a graphical user interface component to the search interface that asks the user how much he or she liked the search result. However, if the feedback mechanism requires the user to provide feedback before he or she can progress further with his or her search, the user may become annoyed and provide incorrect feedback values out of spite. Conversely, if the feedback mechanism does not require the user to provide feedback at all then the overall amount of collected feedback will be diminished as many users will not expend the effort required to give feedback. This research focused on the collection of explicit user feedback in both mandatory (a user must give feedback) and voluntary (a user may give feedback) scenarios. The collected data was used to train a set of decision tree classifiers that provided user satisfaction values as a function of implicit user behavior and a set of search terms. The results of our study indicate that a more accurate classifier can be built from explicit data collected in a voluntary scenario. Given a limited search domain, the classification accuracy can be further improved.

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Marques, Joselene. ""Realimentação de relevância para recuperação por conteúdo de imagens médicas visando diminuir a descontinuidade semântica"." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18062006-204746/.

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O objetivo deste projeto de Mestrado foi o estudo, a análise e o desenvolvimento de técnicas de Realimentação de Relevância (RR) para melhorar a respostas de consultas por similaridade que empregam técnicas de recuperação de imagens por conteúdo (do inglês content-based image retrieval - CBIR). A motivação para o desenvolvimento deste projeto veio do iRIS (internet Retrieval of Images System), que é um protótipo de servidor Web para o processamento de consultas por similaridade, em construção no GBdI (Grupo de Bases de Dados e Imagens) do ICMC-USP. O iRIS pode ser integrado a PACS (Picture and Archiving and Communication System) permitindo que estes possam recuperar imagens por semelhança. A principal restrição do uso de sistemas que incorporam CBIR é a descontinuidade semântica (semantic gap), que credita-se principalmente à utilização de características de baixo nível para descrever as imagens. As características mais utilizadas são baseadas em cor, textura e forma, e geralmente não conseguem mapear o que o usuário deseja/esperar recuperar, gerando um descontentamento do usuário em relação ao sistema. Entretanto, se sistema permitir a iteração do usuário na classificação do conjunto resposta e usar estas informações no processo de refinamento, as consultas podem ser re-processadas e os resultados tendem a atender a expectativa do usuário. Esse é o propósito das técnicas de realimentação de relevância. Este projeto desenvolveu duas técnicas de realimentação de relevância (RR): o RF Projection e o RF Multiple Point Projection. O ganho com a aplicação dessas técnicas foi expressivo, alcançando 29% a mais de precisão sobre a consulta original já na primeira iteração e 42% após 5 iterações. Os experimentos realizados com usuários mostraram que em média são executadas 3 iterações para chegar a um resultado satisfatório. Pelos resultados apresentados nos experimentos, podemos afirmar que RR é uma poderosa ferramenta para impulsionar o uso dos sistemas CBIR e aprimorar as consultas por similaridade.
This Master project aimed at studying, analyzing and developing relevance feedback (RF) techniques to enhance similarity queries that employ the content-based image retrieval (CBIR) approach. The motivation to develop this project came from the iRIS (internet Retrieval of Images System), which is a Web server prototype to process similarity queries. The iRIS can be integrated to a PACS (Picture and Archiving and Communication System) adding the functionality of retrieval images comparing their inherent alikeliness. The main reservation about using CBIR techniques is the semantic gap, because the general use of low level features to describe the images. The low level features, such as color, texture and shape, mostly cannot bridge the gap between what the users expect/want to what they get, generating disappointment and refusal of employing the system. However, if the user is allowed to interact with the system, classifying the query results and using such information on refinement steps, the queries can be reprocessed and the results tend to comply with the users expectation. This is just the core of the relevance feedback techniques. Looking at this scenario, this project developed two relevance feedback (RF) techniques: the RF Projection and the RF Multiple Point Projection. The improvements on the similarity queries were expressive going to up 29% with only one interaction, and to 42% on the fifth interaction, when compared to the original query. Experiments performed with users, have shown us that in average they run 3 iterations before get satisfactory results. By the results given by the experiment, one can claim that RF is a powerful approach to improve the use of CBIR systems and enhance similarity queries.

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Nordin, Alexandra. "Improving an Information Retrieval System by Using Machine Learning to Improve User Relevance Feedback." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185184.

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The aim of this thesis work is to improve the performance of an already existing information retrieval system that uses relevance feedback for performing query expansion. It is a constant goal to improve this system because the docu- ments that are retrieved are a base for various data analysis tasks. It is therefore important that the precision and re- call are high. A user can choose to give relevance feedback when executing a query, meaning the user can mark docu- ments in the search result as relevant or irrelevant and redo the search based on this feedback. The original query will then be expanded based on the user’s feedback. The ap- proach presented in this thesis uses the documents marked as relevant or irrelevant to train a classifier that can classify unknown documents from the search result as either rele- vant, irrelevant or unknown. The aim is to classify unknown documents and add them to the set of feedback documents that are used for the query expansion. The assumption that this thesis is based on is that the more feedback a user gives, the better the query expansion will perform. The system developed in this thesis is evaluated for the English language. The results in this thesis show that integrating the classifier in the existing system improved the perfor- mance in three out of four use cases. The existing system already has a good performance, but small improvements are important. It would therefore be beneficial to integrate it into the existing system.
I detta examensarbetet så är målet att förbättra ett exi- sterande informationssökningssystem som använder sig av relevansåterkoppling för att utföra sökfrågeexpansion. Det finns en konstant efterfrågan att förbättra prestandan av detta system då de dokument som returneras används för olika dataanalysuppgifter. Därför är det viktigt att både precision och täckning är så högt som möjligt. En använ- dare kan välja att ge relevansåterkoppling, vilket betyder att användaren markerar dokument som är relevanta och irrelevanta, vilket sedan används för att utföra sökfråge- expansion. Den initiala sökfrågan expanderas utifrån in- formation från relevansåterkopplingen. Tillvägagångssättet som presenteras i detta arbete använder de markerade do- kumenten för att träna en maskininlärningsmodell som kan klassificera oklassade document som relevanta, irrelevanat eller okända. Målet är att klassificera okända dokument och sedan lägga till dem till uppsättningen av relevansåterkopp- lingsdokument som användaren har markerat. Antagandet som denna metod baseras på är att ju mer relevansåter- koppling som ges, desto bättre sökfrågeexpansion kan sy- stemet utföra. Systemet som utvecklades i detta examens- arbete är byggt för och evaluerat mot data som äs skrivet på engelska. Resultaten i detta arbete visar att denna metod förbättrade resultaten i tre utav fyra testfall. Prestandan för det existerande systemet är redan på en hög nivå, men små förbättringar är viktiga. Det skulle vara en fördel att integrera detta i det existerande systemet.

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Eriksen, Martin. "Rocchio, Ide, Okapi och BIM : En komparativ studie av fyra metoder för relevance feedback." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18877.

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This thesis compares four relevance feedback methods. The Rocchio and Ide dec-hi algorithms for the vector space model and the binary independence model and Okapi BM25 within the probabilistic framework. This is done in a custom-made Information Retrieval system utilizing a collection containing 131 896 LA-Times articles which is part of the TREC ad-hoc collection. The methods are compared on two grounds, using only the relevance information from the 20 highest ranked documents from an initial search and also by using all available relevance information. Although a significant effect of choice of method could be found on the first ground, post-hoc analysis could not determine any statistically significant differences between the methods where Rocchio, Ide dec-hi and Okapi BM25 performed equivalent. All methods except the binary independence model performed significantly better than using no relevance feedback. It was also revealed that although the binary independence model performed far worse on average than the other methods it did outperform them on nearly 20 % of the topics. Further analysis argued that this depends on the lack of query expansion in the binary independence model which is advantageous for some topics although has a negative effect on retrieval efficiency in general. On the second ground Okapi BM25 performed significantly better than the other methods with the binary independence model once again being the worst performer. It was argued that the other methods have problems scaling to large amounts of relevance information where Okapi BM25 has no such issues.
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Xu, Xiaoqian. "Shape Matching, Relevance Feedback, and Indexing with Application to Spine X-Ray Image Retrieval." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1648.pdf.

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Billerbeck, Bodo, and bodob@cs rmit edu au. "Efficient Query Expansion." RMIT University. Computer Science and Information Technology, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20060825.154852.

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Hundreds of millions of users each day search the web and other repositories to meet their information needs. However, queries can fail to find documents due to a mismatch in terminology. Query expansion seeks to address this problem by automatically adding terms from highly ranked documents to the query. While query expansion has been shown to be effective at improving query performance, the gain in effectiveness comes at a cost: expansion is slow and resource-intensive. Current techniques for query expansion use fixed values for key parameters, determined by tuning on test collections. We show that these parameters may not be generally applicable, and, more significantly, that the assumption that the same parameter settings can be used for all queries is invalid. Using detailed experiments, we demonstrate that new methods for choosing parameters must be found. In conventional approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We demonstrate a new method of obtaining expansion terms, based on past user queries that are associated with documents in the collection. The most effective query expansion methods rely on costly retrieval and processing of feedback documents. We explore alternative methods for reducing query-evaluation costs, and propose a new method based on keeping a brief summary of each document in memory. This method allows query expansion to proceed three times faster than previously, while approximating the effectiveness of standard expansion. We investigate the use of document expansion, in which documents are augmented with related terms extracted from the corpus during indexing, as an alternative to query expansion. The overheads at query time are small. We propose and explore a range of corpus-based document expansion techniques and compare them to corpus-based query expansion on TREC data. These experiments show that document expansion delivers at best limited benefits, while query expansion ï¿½ including standard techniques and efficient approaches described in recent work ï¿½ usually delivers good gains. We conclude that document expansion is unpromising, but it is likely that the efficiency of query expansion can be further improved.

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Zellhöfer, David [Verfasser], and Ingo [Akademischer Betreuer] Schmitt. "A preference-based relevance feedback approach for polyrepresentative multimedia retrieval / David Zellhöfer ; Betreuer: Ingo Schmitt." Cottbus : BTU Cottbus - Senftenberg, 2015. http://d-nb.info/1114283843/34.

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Di, Buccio Emanuele. "Design, Implementation and Evaluation of a Methodology for Utilizing Sources of Evidence in Relevance Feedback." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421639.

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The objective of an Information Retrieval system is to support the user when he searches for information by predicting the documents relevant to his information need. Prediction is performed on the basis of evidence available during the search process. User interactions are examples of sources from which this evidence can be gathered. This thesis addresses the problem of uniformly modeling heterogeneous forms of user interaction that are selected as sources for feedback. The problem of uniform source modeling is addressed by way of a complete methodology. The methodology aims at designing, implementing and evaluating a system that validates an experimental hypothesis. The hypothesis being validated regards the possible factors that can explain the user perception of relevance through the evidence gathered from the user interaction. The objective is to obtain and exploit a usable representation of the factors in the role of a new dimension of the information need representation. The methodology aims at being general and not tailored to a specific source. The methodology defines the set of steps needed for obtaining a vector subspace-based representation of the information need dimensions to further exploit this representation for relevance prediction purposes. The set of steps identified are source selection, evidence collection, dimension modeling, document modeling and prediction. This thesis shows how the methodology can be used for modeling two sources of evidence: term relationship in documents judged as relevant and the relationship between interaction features gathered from the behavior of the user when interacting with a set of documents. As for the term relationship dimension, this thesis shows that the current implementation of term relationship is feasible with a very large text collection delivered within the 2009 and 2010 Relevance Feedback tracks of the Text Retrieval Conference initiative. The methodology has supported the evaluation of term relationship for document re-ranking. As for interaction feature relationships, this thesis investigates the adoption of the user behavior dimension for document re-ranking both without query expansion and with query expansion.
L'obiettivo di un sistema di reperimento dell'informazione è quello di supportare l'utente in cerca di informazioni predicendo quali documenti siano rilevanti per la sua esigenza informativa. La predizione di rilevanza è effettuata sulla base dell'evidenza disponibile durante il processo di reperimento. Le interazioni che coivolgono l'utente sono esempi di sorgenti di evidenza. Questa tesi affronta il problema della modellazione uniforme di forme eterogenee di interazione utilizzate come sorgenti di retroazione. Il problema della modellazione uniforme delle sorgenti è affrontato mediante l'introduzione di una metodologia, finalizzata alla progettazione, la realizzazione e la valutazione di un sistema per validare ipotesi sperimentali. Le ipotesi riguardano i possibili fattori che possano spiegare la percezione di rilevanza dell'utente sulla base dell'evidenza ottenuta da interazioni che coinvolgano l'utente stesso. L'obiettivo è quello di ottenere una rappresentazione dei fattori che possa essere utilizzata come una nuova dimensione della rappresentazione dell'esigenza informativa. La metodologia si propone di essere generale e non specifica per una particolare sorgente. Essa definisce una serie di passi necessari per ottenere una rappresentazione in termini di sottospazi delle dimensioni della rappresentazione dell'esigenza informativa per poi utilizzare tale rappresentazione al fine della predizione. La tesi applica la metodologia per modellare due sorgenti di evidenza: le relazioni tra i termini nei documenti giudicati rilevanti e la relazione tra attributi utilizzati per caratterizzare il comportamento dell'utente durante l'interazione con i documenti. In merito alla relazione tra i termini questa tesi mostra come la attuale implementazione per questa sorgente possa essere utilizzata per effettuare il reperimento su collezioni molto ampie, in particolare quelle adottate nelle campagne di valutazione dell'iniziativa Text Retrieval Conference, nello specifico nelle track di Relevance Feedback tenutesi nel 2009 e nel 2010. La metodologia ha consentito di supportare la valutazione del riordinamento dei documenti basato sulle relazioni tra i termini. In merito alle relazioni tra attributi per caratterizzare il comportamento dell'utente questa tesi investiga l'utilizzo di una dimensione basata su tale sorgente per effettuare un riordinamento dei documenti sia unicamente basato sul comportamento, sia mediante espansione dell'interrogazione.

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Bossard, Elaine Ardis. "Examining the roles of frame, frequency, and relevance in performance feedback: exploring evaluative and behavioral outcomes of decision making." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/2048.

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Feedback is often necessary to provide guidance for future decisions, and factors relating to feedback, including the way feedback information is framed, how frequently it is provided, and the relevance of that feedback in relation to one's decision, have been designated as influential for decision making tendencies. Unfortunately, research on what produces the most effective feedback is mixed, and the relationship between these factors and resulting evaluative and behavioral outcomes in less clear. Four studies explored the relationship between feedback frame and frequency by addressing whether overall task feedback framed positively and receiving more frequent trial outcome feedback led to more positive performance evaluations and improvements in subsequent task performance (Studies 1A and 1B), how these evaluative and behavioral outcomes varied across different trial feedback frequency intervals (Study 2A), and whether more relevant trial feedback influenced the pattern of these results (Study 2B). Across the four studies, it was noted that the frequency of trial feedback was more influential for task performance outcomes, while the overall task feedback frame was more influential for performance evaluation outcomes. In addition, more relevant outcome feedback was seen to influence the relationship of feedback variables more for performance evaluations than task performance. Taken together, these studies provide some clarity as to how different types and presentations of feedback produce different evaluative and behavioral outcomes and show initial direction as to when framing task feedback and providing trial feedback more frequently can lead to better, more normatively correct decision making. Theoretical and practical implications, as well as reasons why effects were not consistent across studies, are also discussed.

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com, chungkp@yahoo, and Kien Ping Chung. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Murdoch University, 2007. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20070831.123947.

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Over the last decade, storage of non text-based data in databases has become an increasingly important trend in information management. Image in particular, has been gaining popularity as an alternative, and sometimes more viable, option for information storage. While this presents a wealth of information, it also creates a great problem in retrieving appropriate and relevant information during searching. This has resulted in an enormous growth of interest, and much active research, into the extraction of relevant information from non text-based databases. In particular,content-based image retrieval (CBIR) systems have been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture, and shape or the semantic meaning of the images. To enhance the retrieval speed, most CBIR systems pre-process the images stored in the database. This is because feature extraction algorithms are often computationally expensive. If images are to be retrieved from the World-Wide-Web (WWW), the raw images have to be downloaded and processed in real time. In this case, the feature extraction speed becomes crucial. Ideally, systems should only use those feature extraction algorithms that are most suited for analysing the visual features that capture the common relationship between the images in hand. In this thesis, a statistical discriminant analysis based feature selection framework is proposed. Such a framework is able to select the most appropriate visual feature extraction algorithms by using relevance feedback only on the user labelled samples. The idea is that a smaller image sample group is used to analyse the appropriateness of each visual feature, and only the selected features will be used for image comparison and ranking. As the number of features is less, an improvement in the speed of retrieval is achieved. From experimental results, it is found that the retrieval accuracy for small sample data has also improved. Intelligent E-Business has been used as a case study in this thesis to demonstrate the potential of the framework in the application of image retrieval system. In addition, an inter-query framework has been proposed in this thesis. This framework is also based on the statistical discriminant analysis technique. A common approach in inter-query for a CBIR system is to apply the term-document approach. This is done by treating each images name or address as a term, and the query session as a document. However, scalability becomes an issue with this technique as the number of stored queries increases. Moreover, this approach is not appropriate for a dynamic image database environment. In this thesis, the proposed inter-query framework uses a cluster approach to capture the visual properties common to the previously stored queries. Thus, it is not necessary to memorise the name or address of the images. In order to manage the size of the users profile, the proposed framework also introduces a merging approach to combine clusters that are close-by and similar in their characteristics. Experiments have shown that the proposed framework has outperformed the short term learning approach. It also has the advantage that it eliminates the burden of the complex database maintenance strategies required in the term-document approach commonly needed by the interquery learning framework. Lastly, the proposed inter-query learning framework has been further extended by the incorporation of a new semantic structure. The semantic structure is used to connect the previous queries both visually and semantically. This structure provides the system with the ability to retrieve images that are semantically similar and yet visually different. To do this, an active learning strategy has been incorporated for exploring the structure. Experiments have again shown that the proposed new framework has outperformed the previous framework.

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Smith, Martin Philip. "The effectiveness of document ranking and relevance feedback techniques in a thesaurus-based search intermediary system." Thesis, University of Huddersfield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296001.

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Pöttker, Luciana Maria Vieira [UNESP]. "Arquitetura para recuperação de objetos de aprendizagem – uma abordagem baseada em agentes inteligentes e relevance feedback." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/150090.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Desde a sua criação, a Web tem crescido a um ritmo sem precedentes, situação esta que exigiu mudanças na forma como a sociedade busca e tem acesso à informação. O incremento informacional foi observado em todas as áreas do conhecimento e, desde então, problemas relacionados à recuperação de informação são investigados. No escopo dessa investigação, são pesquisados objetos de aprendizagem que estejam descritos, em um padrão de metadados educacional, e mantidos em repositórios específicos para este fim. Todos os problemas e dificuldades relacionados à recuperação de informação são refletidos no contexto particular dos objetos de aprendizagem. Devido à natureza (multimídia) dos objetos de aprendizagem, a complexidade em recuperá-los se torna mais perceptível. Nesta tese, propõe-se um modelo de arquitetura para recuperação de objetos de aprendizagem baseado em uma integração de tecnologias de sistemas de recuperação de informação, metadados, relevance feedback e agentes inteligentes. O propósito fundamental da arquitetura para recuperação de objetos de aprendizagem é unificar a representação desses recursos educacionais que são disponibilizados em diferentes repositórios e permitir que o usuário realize buscas qualificadas para localizar os objetos de aprendizagem mais adequados para sua necessidade de informação. Esta pesquisa é classificada como qualitativa e de natureza aplicada, uma vez que se relaciona com o problema prático de recuperação de objetos de aprendizagem disponíveis em repositórios da Web. O principal diferencial desta proposta foi de valorizar a inferência do usuário no processo de recuperação de informação, por meio do processo de relevance feedback. Neste processo, o usuário estabelece um diálogo com o sistema de recuperação de informação realizando refinamentos nos resultados que lhe foram retornados. Como esse processo é cíclico, ele pode ser executado até que o usuário esteja satisfeito com os resultados que lhe foram retornados. Conclui-se que um sistema de recuperação de informação é mais eficiente quando amplia seu escopo de recuperação a partir de diferentes fontes de dados e permite a inferência do usuário no julgamento da informação que lhe foi retornada.
The Web has been growing in a record speed since its creation and, therefore, such prospect has demanded changes in the way society seeks for and accesses information. Informational increment was evident in all fields of knowledge and since then, the relevant information retrieval issues have been investigated. In the scope of this investigation, we find researches in learning objects classified into an educational metadata pattern and kept in a specific repository. All the problems and complications related to such retrieval reflect in the learning objects particular context. The complexity in retrieving these learning objects becomes evident given their (multimedia) nature. Here, we suggest an architecture model to retrieve the aforementioned objects that is based on a combination of information retrieval system, metadata, relevance feedback, and intelligent agents. The main purpose of this architecture model is to unify the representation of these educational resources – that are available in a heterogeneous repository – and allow users to perform efficient searches in order to find the most suitable learning objects to their information needs. This is a qualitative and Applied research once it relates to the practical problem of learning objects retrieval available on the Web. The main difference of this suggestion was to value – via relevance feedback – the importance of the user‟s inference in the process of such retrieval, in which the user establishes a dialog with the information retrieval system as to enhance the obtained results, and thus – being a cyclical process – it can be executed until he is pleased them. The conclusion is that an information retrieval system more efficient when its scope is enlarged from the different sources of data and allows the inference of the user when judging what he was presented with.

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Pöttker, Luciana Maria Vieira. "Arquitetura para recuperação de objetos de aprendizagem - uma abordagem baseada em agentes inteligentes e relevance feedback /." Marília, 2017. http://hdl.handle.net/11449/150090.

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Orientador: Edberto Ferneda
Banca: Elvis Fusco
Banca: Walter Moreira
Banca: Ana Carolina Simionato
Banca: Rachel Cristina Vesu Alves
Resumo: Desde a sua criação, a Web tem crescido a um ritmo sem precedentes, situação esta que exigiu mudanças na forma como a sociedade busca e tem acesso à informação. O incremento informacional foi observado em todas as áreas do conhecimento e, desde então, problemas relacionados à recuperação de informação são investigados. No escopo dessa investigação, são pesquisados objetos de aprendizagem que estejam descritos, em um padrão de metadados educacional, e mantidos em repositórios específicos para este fim. Todos os problemas e dificuldades relacionados à recuperação de informação são refletidos no contexto particular dos objetos de aprendizagem. Devido à natureza (multimídia) dos objetos de aprendizagem, a complexidade em recuperá-los se torna mais perceptível. Nesta tese, propõe-se um modelo de arquitetura para recuperação de objetos de aprendizagem baseado em uma integração de tecnologias de sistemas de recuperação de informação, metadados, relevance feedback e agentes inteligentes. O propósito fundamental da arquitetura para recuperação de objetos de aprendizagem é unificar a representação desses recursos educacionais que são disponibilizados em diferentes repositórios e permitir que o usuário realize buscas qualificadas para localizar os objetos de aprendizagem mais adequados para sua necessidade de informação. Esta pesquisa é classificada como qualitativa e de natureza aplicada, uma vez que se relaciona com o problema prático de recuperação de objetos de aprendizagem disponív... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: The Web has been growing in a record speed since its creation and, therefore, such prospect has demanded changes in the way society seeks for and accesses information. Informational increment was evident in all fields of knowledge and since then, the relevant information retrieval issues have been investigated. In the scope of this investigation, we find researches in learning objects classified into an educational metadata pattern and kept in a specific repository. All the problems and complications related to such retrieval reflect in the learning objects particular context. The complexity in retrieving these learning objects becomes evident given their (multimedia) nature. Here, we suggest an architecture model to retrieve the aforementioned objects that is based on a combination of information retrieval system, metadata, relevance feedback, and intelligent agents. The main purpose of this architecture model is to unify the representation of these educational resources - that are available in a heterogeneous repository - and allow users to perform efficient searches in order to find the most suitable learning objects to their information needs. This is a qualitative and applied research once it relates to the practical problem of learning objects retrieval available on the Web. The main difference of this suggestion was to value - via relevance feedback - the importance of the user‟s inference in the process of such retrieval, in which the user establishes a dialog with the information retrieval system as to enhance the obtained results, and thus - being a cyclical process - it can be executed until he is pleased them. The conclusion is that an information retrieval system more efficient when its scope is enlarged from the different sources of data and allows the inference of the user when judging what he was presented with.
Doutor

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Ekberg-Selander, Karin, and Johanna Enberg. "Query Expansion : en jämförande studie av Automatisk Query Expansion med och utan relevans-feedback." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18416.

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In query expansion (QE) terms are added to an initial query in order to improve retrieval effectiveness. In this thesis we use QE in the sense that a reformulation of the query is done by deleting the terms in the initial query and instead replacing them with terms from the documents retrieved in the initial run. The aim of this thesis is to, in a experimental full text invironment, study and compare the retrieval result of two different query expansion strategies in relation to each other. The following questions are addressed by the study: How do the two strategies perform in relation to each other regarding recall?What may be causing the result?Are the two strategies retrieving the same relevant documents?Two strategies are designed to simulate a searcher using automatic query expansion (AQE) either with or without relevance feedback. Strategy I is simulating AQE without relevance feedback by taking the top five documents that are retrieved in the initial run and then extracting the top ten most frequently occurring terms in these to create a new query. Correspondingly the Strategy II, is simulating AQE with relevance feedback by taking the top five relevant documents and extracting the top ten terms in these to create a new query. It is concluded that both of the strategies’ retrieval performance was improved for most of the topics. In average Strategy II did achieve 54.63 percent recall compared to Strategy I which did achieve 45.59 percent recall. The two strategies did retrieve different relevant documents for majority of the topics. Hence, it would be reasonable to base a system on both of them.
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Lipani, Aldo. "Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4528/.

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The central objective of research in Information Retrieval (IR) is to discover new techniques to retrieve relevant information in order to satisfy an Information Need. The Information Need is satisfied when relevant information can be provided to the user. In IR, relevance is a fundamental concept which has changed over time, from popular to personal, i.e., what was considered relevant before was information for the whole population, but what is considered relevant now is specific information for each user. Hence, there is a need to connect the behavior of the system to the condition of a particular person and his social context; thereby an interdisciplinary sector called Human-Centered Computing was born. For the modern search engine, the information extracted for the individual user is crucial. According to the Personalized Search (PS), two different techniques are necessary to personalize a search: contextualization (interconnected conditions that occur in an activity), and individualization (characteristics that distinguish an individual). This movement of focus to the individual's need undermines the rigid linearity of the classical model overtaken the ``berry picking'' model which explains that the terms change thanks to the informational feedback received from the search activity introducing the concept of evolution of search terms. The development of Information Foraging theory, which observed the correlations between animal foraging and human information foraging, also contributed to this transformation through attempts to optimize the cost-benefit ratio. This thesis arose from the need to satisfy human individuality when searching for information, and it develops a synergistic collaboration between the frontiers of technological innovation and the recent advances in IR. The search method developed exploits what is relevant for the user by changing radically the way in which an Information Need is expressed, because now it is expressed through the generation of the query and its own context. As a matter of fact the method was born under the pretense to improve the quality of search by rewriting the query based on the contexts automatically generated from a local knowledge base. Furthermore, the idea of optimizing each IR system has led to develop it as a middleware of interaction between the user and the IR system. Thereby the system has just two possible actions: rewriting the query, and reordering the result. Equivalent actions to the approach was described from the PS that generally exploits information derived from analysis of user behavior, while the proposed approach exploits knowledge provided by the user. The thesis went further to generate a novel method for an assessment procedure, according to the "Cranfield paradigm", in order to evaluate this type of IR systems. The results achieved are interesting considering both the effectiveness achieved and the innovative approach undertaken together with the several applications inspired using a local knowledge base.

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Bot, Razvan Stefan. "Improving document representation by accumulating relevance feedback the relevance feedback accumulation (RFA) algorithm /." Thesis, 2005. http://library1.njit.edu/etd/fromwebvoyage.cfm?id=njit-etd2005-127.

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"World-wide web information discovery via relevance feedback." 1998. http://library.cuhk.edu.hk/record=b5889686.

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Yue Che Wang, Kenneth.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaves 100-106).
Abstract also in Chinese.
Abstract --- p.i
Abstract (Chinese) --- p.iv
Acknowledgement --- p.vi
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- The World-Wide Web --- p.1
Chapter 1.2 --- Searching Information on the WWW --- p.2
Chapter 1.3 --- Intelligent content-based information discovery on the Web --- p.4
Chapter 1.4 --- Organization of the Thesis --- p.7
Chapter 2 --- Literature Review --- p.9
Chapter 2.1 --- Search Engines --- p.9
Chapter 2.2 --- Information Indexing Systems --- p.11
Chapter 2.3 --- Agent-based Systems --- p.13
Chapter 2.4 --- Information Filtering Systems --- p.16
Chapter 3 --- Overview of the Proposed Approach --- p.20
Chapter 3.1 --- System Architecture --- p.21
Chapter 3.2 --- Topic Profile Specification --- p.25
Chapter 3.3 --- Text Representation --- p.29
Chapter 3.3.1 --- Profile Feature Representation --- p.30
Chapter 3.3.2 --- Document Feature Representation --- p.33
Chapter 3.4 --- Advantages of the Topic Profile Specifications --- p.34
Chapter 4 --- Relevance Score Evaluation Process and Relevance Feedback Model --- p.36
Chapter 4.1 --- Term Weights --- p.37
Chapter 4.2 --- Document Evaluation through Relevance Score --- p.39
Chapter 4.3 --- Learning via Relevance Feedback --- p.42
Chapter 4.3.1 --- Introduction to Relevance Feedback --- p.43
Chapter 4.3.2 --- Feature Extraction from the Relevance Feedback Models --- p.44
Chapter 4.3.3 --- Topic Feature Vectors Refinement --- p.49
Chapter 5 --- Intelligent Web Exploration --- p.51
Chapter 5.1 --- Introduction to Simulated Annealing --- p.51
Chapter 5.2 --- Intelligent Web Exploration by Simulated Annealing --- p.54
Chapter 5.2.1 --- Mathematical Setting of the Discovery Process --- p.57
Chapter 5.2.2 --- The Entire Exploration Algorithm --- p.58
Chapter 5.3 --- Incorporating with the Relevance Feedback Model --- p.60
Chapter 6 --- Experimental Results --- p.61
Chapter 6.1 --- The Design of the Experiments --- p.61
Chapter 6.2 --- Experiments on the Effects of the Simulated Annealing Schedule upon the Discovery Precision --- p.65
Chapter 6.2.1 --- Experiment Setup --- p.66
Chapter 6.2.2 --- Results --- p.66
Chapter 6.3 --- Experiments on the Index Page Topic Profile Specification --- p.72
Chapter 6.3.1 --- Experiment Setup --- p.72
Chapter 6.3.2 --- Results --- p.73
Chapter 6.4 --- Experiments on the Relevance Feedback with Full-Text Feature Extraction Strategy --- p.75
Chapter 6.4.1 --- Experiment Setup --- p.75
Chapter 6.4.2 --- Results --- p.76
Chapter 6.5 --- Comparisons of the Relevance Feedback Feature Extraction Strate- gies --- p.78
Chapter 6.5.1 --- Experiment Setup --- p.78
Chapter 6.5.2 --- Results --- p.79
Chapter 6.6 --- Comparisons between the Example Page and the Keyword Topic Profile Specifications --- p.82
Chapter 6.6.1 --- Experiment Setup --- p.83
Chapter 6.6.2 --- Results --- p.83
Chapter 6.7 --- Summary from the Experimental Results --- p.87
Chapter 7 --- Conclusion --- p.91
Chapter 7.1 --- The Aim of Our Proposed System --- p.91
Chapter 7.2 --- The Favorable Features and the Effectiveness of Our Proposed System --- p.92
Chapter 7.3 --- Future Work --- p.94
Appendix --- p.96
Chapter A --- List of URLs for the Example Pages --- p.96
Chapter B --- List of URLs for the Arbitrarily Chosen Index Pages --- p.98
Bibliography --- p.100

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Lo, Yi-Jong, and 羅一中. "Using non-relevance feedback to enhance personalized search." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/3c49e3.

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碩士
國立臺北科技大學
資訊工程系研究所
98
With advances in Web technology, people can easily search lots of information on the Web by using search engines. Given user queries, general Web search only cares about the relevant documents for most of the users. Thus, personalized search has become an important research topic since it concerns about different user’s preferences. Now there are several ways to implement personalized search: predefining user custom search, classifying user preferences by analyzing their previous behaviors, and using collaborative filtering or recommender systems to find items from similar users to give recommendations. This paper proposes two suggestions. First, we add non-relevant items in search results to avoid inconsistent feedback. The goal is filtering out incorrect user behavior. Second, we propose a dynamic proportional personalized search system that mix personal search and click history, similar user history, and general search result according to their adaptive proportions. From the experimental results, users can find what they want with a higher accuracy by using our search system. The results also show that users get a higher satisfaction score with our system.

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Lin, Shen-mu, and 林伸穆. "Applying Novel Relevance Feedback in Query Expansion Enhancement." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/28437136319094505566.

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碩士
國立雲林科技大學
資訊管理系碩士班
94
Query Expansion was designed to overcome the barren query words issued by the user and has been applied in many commercial products. This treatment tries to expand query words to identify users’ real requirement based on semantic computation. It may be critical to deal with the problem of information overloading and diminish the using threshold, however the modern retrieval systems usually lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. In this study, we propose the LLSF method based on each individual search history to automatically generate specific personalized profile matrix. By which to generate context-based expanded query words. Considering the accuracy of retrieving performance, we process query words re-weighting and document pooling algorithm to achieve this goal. Finally, the documents list is ranked by the way of stressed density distribution modeling. And the experimental results show that our framework corresponds to personalization and the performance is very promising.

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Lin, Yi-Lain, and 林宜聯. "A Study of Image Retrieval and Relevance Feedback." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/13129725875136801854.

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碩士
國立東華大學
資訊工程學系
92
Content-Based Image Retrieval (CBIR) takes visual features, instead of textual annotations, of images as the keys to retrieval images. This thesis presents a novel feature called “frequency layer” that reveals both the color and shape of image contents with respect to different frequencies of textures from the perspective of human vision. As every user may have his own subjective perception on the same image contents, each extracted key of visual feature should be of different importance for different users. This thesis also proposes two methods of relevance feedback to handle this problem of users’ subjectivity. One is designed on a neural network (NN) approach, while the other is based on the technique of maximum likelihood estimation (MLE). In to the experimental results in this research work, the performance of both methods are evaluated and compared. The results on testing some image databases show that both methods are effective. Moreover, the MLE method outperforms the NN approach.

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Wu, Ke-neng, and 吳克能. "Applying Relevance Feedback to Improving Text Classification Performance." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/86044801840439396200.

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碩士
國立中央大學
資訊管理研究所
99
With the rapid development of the Internet, the information explosion across the Internet offers access to an increasing amount of information. Information retrieval system is playing an important role in the information retrieval process. In order to improve the retrieval quality and provide information in line with users’ need, “text classification” is an important issue. The study proposes an approach extracting information of relevance feedback to construct user profile for feature selection and term weighting adjustment of documents, and this approach consists of two concepts: (1) The user profile represents positive and negative interests of user, and the documents preserve only the features belonging to the user profile for reducing the noise interference in text classification. (2) The terms appearing in the user profile or important position in document are weighted for increasing the characteristic difference between relevant and non-relevant documents. Characteristic enhancement of documents is the application of term sensitivity aided by semi-structured information. The results of the experiments show that the proposed approach can extract information of relevance feedback effectively. Not only improving the accuracy of text classification but also at least a half of processing time can be greatly reduced.

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Lee, Hung-Yi, and 李宏毅. "Spoken Content Retrieval - Relevance Feedback, Graphs and Semantics." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/93584222510389974705.

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博士
國立臺灣大學
電信工程學研究所
100
Multimedia content over the Internet is very attractive, while the spoken part of such content very often tells the core information. Therefore, spoken content retrieval will be very important in helping users retrieve and browse efficiently across the huge qualities of multimedia content in the future. There are usually two stages in typical spoken content retrieval approaches. In the first stage, the audio content is recognized into text symbols by an Automatic Speech Recognition (ASR) system based on a set of acoustic models and language models. In the second stage, after the user enters a query, the retrieval engine searches through the recognition output and returns to the user a list of relevant spoken documents or segments. If the spoken content can be transcribed into text with very high accuracy, the problem is naturally reduced to text information retrieval. However, the inevitable high recognition error rates for spontaneous speech under a wide variety of acoustic conditions and linguistic context make this never possible. In this thesis, the above standard two-stage architecture is completely broken, and the two stages of recognition and retrieval are mixed up and considered as a whole. A set of approaches beyond retrieving over recognition output has been developed here. This idea is very helpful for spoken content retrieval, and may become one of the main future directions in this area. To consider the two stages of recognition and retrieval as a whole, it is proposed to adjust the acoustic model parameters borrowing the techniques of discriminative training but based on user relevance feedback. The problem of retrieval oriented acoustic model re-estimation is different from the conventional acoustic model training approaches for speech recognition in at least two ways: 1. The model training information includes only whether a spoken segment is relevant to a query or not; it does not include the transcription of any utterance. 2. The goal is to improve retrieval performance rather than recognition accuracy. A set of objective functions for retrieval oriented acoustic model re-estimation is proposed to take the properties of retrieval into consideration. There have been some previous works in spoken content retrieval taking advantage of the discriminative capability of machine learning methods. Different from the previous works that derive information from recognition output as features, acoustic vectors such as MFCC are taken as the features for discriminating relevant and irrelevant segments, and they are successfully applied on the scenario of Pseudo Relevance Feedback (PRF). The recognition process can be considered as ``quantization'', in which the acoustic vector sequences are quantized into word symbols. Because different vector sequences may be quantized into the same symbol, much of the information in the spoken content may be lost in the stage of speech recognition. Information directly from the acoustic vector space is considered to compensate for the recognition output in this thesis. This is realized by either PRF or a graph-based re-ranking approach considering the similarity structure among all the segments retrieved. This approach is successfully applied on not only word-based retrieval system but also subword-based system, and these approaches improve the results of Out-of-Vocabulary (OOV) queries as well. The task of Spoken Term Detection (STD) is mainly considered in this thesis, for which the goal is simply returning spoken segments that contain the query terms. Although most works in spoken content retrieval nowadays continue to focus on STD, in this thesis a more general task is also considered: to retrieve the spoken documents semantically related to the queries, no matter the query terms are included in the spoken documents or not. Taking ASR transcriptions as text, the techniques such as latent semantic analysis or query expansion developed for text-based information retrieval can be directly applied for this task. However, the inevitable recognition errors in ASR transcriptions degrade the performance of these techniques. To have more robust semantic retrieval of spoken documents, the expected term frequencies derived from the lattices are enhanced by acoustic similarity with a graph-based approach. The enhanced term frequencies improve the performance of language modelling retrieval approach, document expansion techniques based on latent semantic analysis, and query expansion methods considering both words and latent topic information.

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Ming-Han, Hsieh. "Region-Based Image Retrieval by Use of Relevance Feedback." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200518462800.

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41

Li, Jin-Der, and 李進德. "Content-based Image Retrieval with Object-based Relevance Feedback." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/66283677272079652553.

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碩士
義守大學
資訊工程學系
88
Content-based retrieval is the most convenient way for querying multimedia data. It has become one of the most important research directions on multimedia information retrieval. Many related researches have been proposed in the past years. In this thesis, we propose a new approach for content-based image retrieval and implement a prototype to evaluate the efficiency and effectiveness of the proposed approach. Three kinds of features in images including color, shape and spatial relationship are used to measure the similarity between two images. The proposed approach extracts the different features from images automatically and stores them in a uniform representation. Two main similarity measures consisting of the object-pairs matching algorithm and the similarity matching algorithm are developed to combine with the relevance feedback mechanism for capturing the user’s information need by interactive user interface. The experimental results are evaluated by precision-recall model, and show that the proposed approach is effective.

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42

"Learning on relevance feedback in content-based image retrieval." 2004. http://library.cuhk.edu.hk/record=b5892070.

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Hoi, Chu-Hong.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (leaves 89-103).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.iv
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Content-based Image Retrieval --- p.1
Chapter 1.2 --- Relevance Feedback --- p.3
Chapter 1.3 --- Contributions --- p.4
Chapter 1.4 --- Organization of This Work --- p.6
Chapter 2 --- Background --- p.8
Chapter 2.1 --- Relevance Feedback --- p.8
Chapter 2.1.1 --- Heuristic Weighting Methods --- p.9
Chapter 2.1.2 --- Optimization Formulations --- p.10
Chapter 2.1.3 --- Various Machine Learning Techniques --- p.11
Chapter 2.2 --- Support Vector Machines --- p.12
Chapter 2.2.1 --- Setting of the Learning Problem --- p.12
Chapter 2.2.2 --- Optimal Separating Hyperplane --- p.13
Chapter 2.2.3 --- Soft-Margin Support Vector Machine --- p.15
Chapter 2.2.4 --- One-Class Support Vector Machine --- p.16
Chapter 3 --- Relevance Feedback with Biased SVM --- p.18
Chapter 3.1 --- Introduction --- p.18
Chapter 3.2 --- Biased Support Vector Machine --- p.19
Chapter 3.3 --- Relevance Feedback Using Biased SVM --- p.22
Chapter 3.3.1 --- Advantages of BSVM in Relevance Feedback --- p.22
Chapter 3.3.2 --- Relevance Feedback Algorithm by BSVM --- p.23
Chapter 3.4 --- Experiments --- p.24
Chapter 3.4.1 --- Datasets --- p.24
Chapter 3.4.2 --- Image Representation --- p.25
Chapter 3.4.3 --- Experimental Results --- p.26
Chapter 3.5 --- Discussions --- p.29
Chapter 3.6 --- Summary --- p.30
Chapter 4 --- Optimizing Learning with SVM Constraint --- p.31
Chapter 4.1 --- Introduction --- p.31
Chapter 4.2 --- Related Work and Motivation --- p.33
Chapter 4.3 --- Optimizing Learning with SVM Constraint --- p.35
Chapter 4.3.1 --- Problem Formulation and Notations --- p.35
Chapter 4.3.2 --- Learning boundaries with SVM --- p.35
Chapter 4.3.3 --- OPL for the Optimal Distance Function --- p.38
Chapter 4.3.4 --- Overall Similarity Measure with OPL and SVM --- p.40
Chapter 4.4 --- Experiments --- p.41
Chapter 4.4.1 --- Datasets --- p.41
Chapter 4.4.2 --- Image Representation --- p.42
Chapter 4.4.3 --- Performance Evaluation --- p.43
Chapter 4.4.4 --- Complexity and Time Cost Evaluation --- p.45
Chapter 4.5 --- Discussions --- p.47
Chapter 4.6 --- Summary --- p.48
Chapter 5 --- Group-based Relevance Feedback --- p.49
Chapter 5.1 --- Introduction --- p.49
Chapter 5.2 --- SVM Ensembles --- p.50
Chapter 5.3 --- Group-based Relevance Feedback Using SVM Ensembles --- p.51
Chapter 5.3.1 --- (x+l)-class Assumption --- p.51
Chapter 5.3.2 --- Proposed Architecture --- p.52
Chapter 5.3.3 --- Strategy for SVM Combination and Group Ag- gregation --- p.52
Chapter 5.4 --- Experiments --- p.54
Chapter 5.4.1 --- Experimental Implementation --- p.54
Chapter 5.4.2 --- Performance Evaluation --- p.55
Chapter 5.5 --- Discussions --- p.56
Chapter 5.6 --- Summary --- p.57
Chapter 6 --- Log-based Relevance Feedback --- p.58
Chapter 6.1 --- Introduction --- p.58
Chapter 6.2 --- Related Work and Motivation --- p.60
Chapter 6.3 --- Log-based Relevance Feedback Using SLSVM --- p.61
Chapter 6.3.1 --- Problem Statement --- p.61
Chapter 6.3.2 --- Soft Label Support Vector Machine --- p.62
Chapter 6.3.3 --- LRF Algorithm by SLSVM --- p.64
Chapter 6.4 --- Experimental Results --- p.66
Chapter 6.4.1 --- Datasets --- p.66
Chapter 6.4.2 --- Image Representation --- p.66
Chapter 6.4.3 --- Experimental Setup --- p.67
Chapter 6.4.4 --- Performance Comparison --- p.68
Chapter 6.5 --- Discussions --- p.73
Chapter 6.6 --- Summary --- p.75
Chapter 7 --- Application: Web Image Learning --- p.76
Chapter 7.1 --- Introduction --- p.76
Chapter 7.2 --- A Learning Scheme for Searching Semantic Concepts --- p.77
Chapter 7.2.1 --- Searching and Clustering Web Images --- p.78
Chapter 7.2.2 --- Learning Semantic Concepts with Relevance Feed- back --- p.73
Chapter 7.3 --- Experimental Results --- p.79
Chapter 7.3.1 --- Dataset and Features --- p.79
Chapter 7.3.2 --- Performance Evaluation --- p.80
Chapter 7.4 --- Discussions --- p.82
Chapter 7.5 --- Summary --- p.82
Chapter 8 --- Conclusions and Future Work --- p.84
Chapter 8.1 --- Conclusions --- p.84
Chapter 8.2 --- Future Work --- p.85
Chapter A --- List of Publications --- p.87
Bibliography --- p.103

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43

Wu, Jyun-Yue, and 吳君岳. "Adaptive Image Retrieval by Relevance Feedback onPositive/Negative Examples." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/hhns27.

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碩士
國立東華大學
資訊工程學系
95
It is a difficult but need to proceeding task to understand users’ demand in a content-based image retrieval system. The subjectivity of different users will affect the retrieval results. Relevance feedback(RF) is an effective tool for taking the user’s subjectivity into account. RF provides a scheme that understands the user’s demand and reduces the subjectivity of different users. In this paper, we present a new RF framework: there are two image sets, one is the positive images set and the other one is the negative images set which are both classified by the users. After the classification, the maximum likelihood estimator is used to find the best query points and feature weights in the two image sets individually. Through the learning scheme, the parameters of the two image sets are adjusted by the retrieval results to approach the user’s demand and improve the retrieval efficiency. The experimental results show that the relevance feedback method proposed in this paper compares with some existing methods.

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44

Hsieh, Ming-Han, and 謝明翰. "Region-Based Image Retrieval by Use of Relevance Feedback." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/83936610906625897051.

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碩士
國立臺灣大學
資訊工程學研究所
93
With the exponential growth of multi-media data, finding images in a large database has become more difficult. Region-based image retrieval (RBIR) is used for solving this problem in this thesis. There are some differences between RBIR and traditional content-based image retrieval (CBIR) systems. CBIR is focused on the similarity of global images and RBIR is focused on the similarity of the local image regions. We apply the watershed segmentation to segment each image into some regions. To classify these regions, the fuzzy k-means clustering algorithm is time-wasting and uses too much space to store the information about the regions. We propose a modified fuzzy k-means clustering algorithm to classify regions efficiently. In order to accelerate our system, we propose a new method for filtering which can filter out many unsuitable images. The candidate images are ranked based on their similarity measure. After our system retrieves the images, the user is able to give feedback to the system. Based on user’s feedback information, our system will retrieve the images that are even closer to the user’s intent.

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45

Lan, Ing-Ren, and 藍英仁. "Relevance Feedback for Image Retrieval Using Multi-color Descriptor." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/91847104280984370288.

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碩士
義守大學
資訊工程學系
92
In the recent years, digital multimedia has been applied for various applications. Effective management becomes an important role for retrieval from database. This thesis aims at building a content-based retrieving system to retrieve desired images from database. There are two main contributions. First, we propose an image feature database based on human’s perception. This method collects the information of center region in an image, The system is divided into a global feature database, regional feature database to the choice of the feature database while searching. The system automatically chooses the suitable feature database in the method to adopt adaptability. Second, we develop a new feedback technique to identify the user’s desire. In this technique, the weights of features are updated based on user’s recognition. Experiment results indicate that the proposed method is effective for content-based image retrieving.

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46

Yan, Rung-Da, and 顏榮達. "Interactive MPEG-4 Video Retrieval System with Relevance Feedback." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/87345185491039142089.

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Yang, Duen-Chi, and 楊敦淇. "Using Relevance Feedback in Bayesian Probabilistic Mixture Retrieval Model." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/19440194867358068355.

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碩士
國立成功大學
資訊工程學系碩博士班
91
A procedure is studied for the purpose of retrieval efficiency improvement for text data to save the cost for who eager for information. In this thesis, a new relevance feedback technique is developed and applied for information retrieval. We focus on automatically adapting model parameters to improve the retrieval performance. Traditionally, the “query expansion” and “query term reweighting” are viewed as two popular of relevance feedback approaches. In this study, the retrieval framework is based on the mixture N-gram model. To improve the retrieval performance, we apply the techniques of query expansion as well as query term rewighting for relevance feedback. Furthermore, the top N retrieved documents at the previous iteration are used for adapting query relevent to a new query document model which incorporates more information and is more useful in the retrieval process. Both query term reweighting and document language model adaptation apply ML (Maximum Likelihood) estimation and Expectation-Maximization (EM) algorithm to estimation the best parameters. In the experiments, the Xinhua news of TDT2 corpus are adopted. We find that the experimental results using query term reweighting and document model adaptation are desirable. If we combine three relevance feedback approaches, the results are further improved compared to using individual approach. Map language model adaptation achieves better performance than ML adaptation in the information retrieval system.

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48

Tao, Ju-Lan, and 陶如蘭. "Content-Based Image Retrieval by Use of Relevance Feedback." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/31603013491059749097.

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碩士
國立臺灣大學
資訊工程學研究所
90
The aim of this thesis is to build a content-based image retrieval system. It is to retrieve the desired images for a user from a large image database, based on the image contents. In the thesis, a system is implemented and two contributions are made for content-based image retrieval. One is the design of a new feature based on the results of multi-scale watershed image segmentation, which measures the distribution of colors and sizes of segmented regions. The other one is the derivation of a new method for relevance feedback based on Bayesian formulation. In this method, the problem of content-based image retrieval is first formulated as a two-class classification problem, where each image in the database is classified as either “relevant” or “non-relevant” with respect to the query and the goal is to minimize the misclassification error. Next, the problem of image retrieval is transferred into a simpler problem of ranking each image in the database by using a similarity measure that is basically a likelihood ratio. The experimental results have indicated that the proposed method has potential to become practical for content-based image retrieval.

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Liu, Yi-Min, and 柳依旻. "Adaptive Relevance Feedback Techniques for Content-Based Image Retrieval." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/18126839365646713051.

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碩士
國立暨南國際大學
資訊管理學系
95
Due to the popularity of Internet and the growing demand of image access, the volume of image databases is exploding. Hence, we need a more efficient and effective image searching technology. Relevance feedback (RF) is an interaction process between the user and the system such that the user’s information need is satisfied by the retrievals from the system. Traditional RF techniques use the same system parameter values for all types of query images. It is questionable that the best performance can be obtained through such setting. Hence, we propose self-adapting parameterization for the traditional relevance feedback approaches including the query vector modification (QVM) and the feature relevance estimation (FRE) methods using the particle swarm optimization. As such different system parameter values can be used to handle various types of queries, the retrieval system is thus more efficient and effective.

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Chan, Li-Wei. "Content-Based Object Movie Retrieval by Use of Relevance Feedback." 2004. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200419433500.

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