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Journal articles on the topic 'Relevance feedback'

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Published: 4 June 2021
Last updated: 8 June 2024

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1

Raiber, Fiana, and Oren Kurland. "Relevance Feedback." ACM Transactions on Information Systems 37, no. 4 (December 10, 2019): 1–28. http://dx.doi.org/10.1145/3360487.

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2

Giorgi, D., P. Frosini, M. Spagnuolo, and B. Falcidieno. "3D relevance feedback via multilevel relevance judgements." Visual Computer 26, no. 10 (August 14, 2010): 1321–38. http://dx.doi.org/10.1007/s00371-010-0524-0.

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3

Zhou, Dong, Mark Truran, Jianxun Liu, and Sanrong Zhang. "Collaborative pseudo-relevance feedback." Expert Systems with Applications 40, no. 17 (December 2013): 6805–12. http://dx.doi.org/10.1016/j.eswa.2013.06.030.

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4

Premkumar, M., and R. Sowmya. "Interactive Content Based Image Retrieval using Multiuser Feedback." JOIV : International Journal on Informatics Visualization 1, no. 4 (December 1, 2017): 165. http://dx.doi.org/10.30630/joiv.1.4.57.

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Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.
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5

Chen, Zhaofeng, Naixuan Guo, Jiu Sun, Yuanyuan Wang, Feng Zhou, Sen Xu, and Rugang Wang. "Pseudo-Relevance Feedback Method Based on the Topic Relevance Model." Mathematical Problems in Engineering 2022 (July 7, 2022): 1–6. http://dx.doi.org/10.1155/2022/1697950.

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In the field of information retrieval, most pseudo-relevance feedback models select candidate terms from the top k documents returned by the first-pass retrieval, but they cannot identify the reliability of these documents. This paper proposed a new approach to obtain feedback information more comprehensively by constructing four corresponding models. Firstly, the algorithm incorporated topic-based relevance information into the relevance model RM3 and constructed a topic-based relevance model, denoted as TopRM3, with two corresponding variants. TopRM3 estimated the reliability of a feedback document in language modeling when executing pseudo-relevance feedback from both term and topic-based perspectives. Secondly, the algorithm introduced topic-based relevance information into Rocchio’s model and constructed the corresponding model, denoted as TopRoc, with two corresponding variants. Experimental results on the five TREC collections show that the proposed TopRM3 and TopRoc are effective and generally superior to the state-of-the-art pseudo-relevance feedback models with optimal parameter settings in terms of mean average precision.
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6

Panyr, Jiri. "Conceptual Clustering and Relevance Feedback." KNOWLEDGE ORGANIZATION 14, no. 3 (1987): 133–37. http://dx.doi.org/10.5771/0943-7444-1987-3-133.

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7

Crouch, Carolyn J. "Relevance feedback at INEX 2005." ACM SIGIR Forum 40, no. 1 (June 2006): 58–59. http://dx.doi.org/10.1145/1147197.1147208.

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8

Algarni, Abdulmohsen, Yuefeng Li, Sheng-Tang Wu, and Yue Xu. "Text mining in negative relevance feedback." Web Intelligence and Agent Systems: An International Journal 10, no. 2 (2012): 151–63. http://dx.doi.org/10.3233/wia-2012-0238.

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9

Patil, Pushpa B., and Manesh Kokare. "Semantic Image Retrieval Using Relevance Feedback." International journal of Web & Semantic Technology 2, no. 4 (October 30, 2011): 139–48. http://dx.doi.org/10.5121/ijwest.2011.2411.

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10

Tao, Dacheng, Xuelong Li, and Stephen J. Maybank. "Negative Samples Analysis in Relevance Feedback." IEEE Transactions on Knowledge and Data Engineering 19, no. 4 (April 2007): 568–80. http://dx.doi.org/10.1109/tkde.2007.1003.

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11

Parapar, Javier, Manuel A. Presedo-Quindimil, and Álvaro Barreiro. "Score distributions for Pseudo Relevance Feedback." Information Sciences 273 (July 2014): 171–81. http://dx.doi.org/10.1016/j.ins.2014.03.034.

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12

Lee, Chu-Hui, and Meng-Feng Lin. "Ego-similarity measurement for relevance feedback." Expert Systems with Applications 37, no. 1 (January 2010): 871–77. http://dx.doi.org/10.1016/j.eswa.2009.05.101.

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13

Joshi, Sandeep, and Satpal Singh Kushwaha. "Query Expansion using Artificial Relevance Feedback." International Journal of Computer Applications 44, no. 7 (April 30, 2012): 41–45. http://dx.doi.org/10.5120/6279-8448.

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14

Salton, Gerard, and Chris Buckley. "Improving retrieval performance by relevance feedback." Journal of the American Society for Information Science 41, no. 4 (June 1990): 288–97. http://dx.doi.org/10.1002/(sici)1097-4571(199006)41:4<288::aid-asi8>3.0.co;2-h.

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15

Widyanto, M. Rahmat, and Tatik Maftukhah. "Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (January 20, 2010): 34–38. http://dx.doi.org/10.20965/jaciii.2010.p0034.

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Fuzzy relevance feedback using Query Vector Modification (QVM) method in image retrieval is proposed. For feedback, the proposed six relevance levels are: “very relevant”, “relevant”, “few relevant”, “vague”, “not relevant”, and “very non relevant”. For computation of user feedback result, QVM method is proposed. The QVM method repeatedly reformulates the query vector through user feedback. The system derives the image similarity by computing the Euclidean distance, and computation of color parameter value by Red, Green, and Blue (RGB) color model. Five steps for fuzzy relevance feedback are: image similarity, output image, computation of membership value, feedback computation, and feedback result. Experiments used QVM method for six relevance levels. Fuzzy relevance feedback using QVM method gives higher precision value than conventional relevance feedback method. Experimental results show that the precision value improved by 28.56% and recall value improved 3.2% of conventional relevance feedback. That indicated performance Image Retrieval System can be improved by fuzzy relevance feedback using QVM method.
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16

Picariello, Antonio, and Antonio M. Rinaldi. "User Relevance Feedback in Semantic Information Retrieval." International Journal of Intelligent Information Technologies 3, no. 2 (April 2007): 36–50. http://dx.doi.org/10.4018/jiit.2007040103.

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17

Hamdy, A. "REGION-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK." JES. Journal of Engineering Sciences 40, no. 3 (May 1, 2012): 819–32. http://dx.doi.org/10.21608/jesaun.2012.114412.

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18

Jing, F., M. Li, H. J. Zhang, and B. Zhang. "Relevance Feedback in Region-Based Image Retrieval." IEEE Transactions on Circuits and Systems for Video Technology 14, no. 5 (May 2004): 672–81. http://dx.doi.org/10.1109/tcsvt.2004.826775.

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19

Crouch, Carolyn. "Relevance feedback at the INEX 2004 workshop." ACM SIGIR Forum 39, no. 1 (June 2005): 41–42. http://dx.doi.org/10.1145/1067268.1067282.

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20

Sakai, Tetsuya, Toshihiko Manabe, and Makoto Koyama. "Flexible pseudo-relevance feedback via selective sampling." ACM Transactions on Asian Language Information Processing 4, no. 2 (June 2005): 111–35. http://dx.doi.org/10.1145/1105696.1105699.

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21

Balakrishnan, Vimala, Kian Ahmadi, and Sri Devi Ravana. "Improving retrieval relevance using users’ explicit feedback." Aslib Journal of Information Management 68, no. 1 (December 31, 2015): 76–98. http://dx.doi.org/10.1108/ajim-07-2015-0106.

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Purpose – The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback. Design/methodology/approach – CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is further enhanced using case-based reasoning in retrieving the top-5 results. A search engine prototype was developed using Text REtrieval Conference as the document collection, and results were evaluated at three levels (i.e. top-5, 10 and 15). A user evaluation involving 28 students was administered, focussing on 20 queries. Findings – Both Mean Average Precision and Normalized Discounted Cumulative Gain results indicate CoRRe to have the highest retrieval precisions at all the three levels compared to the other feedback models. Furthermore, independent t-tests showed the precision differences to be significant. Rating was found to be the most popular technique among the participants, producing the best precision compared to referral and comments. Research limitations/implications – The findings suggest that search retrieval relevance can be significantly improved when users’ explicit feedback are integrated, therefore web-based systems should find ways to manipulate users’ feedback to provide better recommendations or search results to the users. Originality/value – The study is novel in the sense that users’ comment, rating and referral were taken into consideration to improve their overall search experience.
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22

Melucci, Massimo. "Relevance Feedback Algorithms Inspired By Quantum Detection." IEEE Transactions on Knowledge and Data Engineering 28, no. 4 (April 1, 2016): 1022–34. http://dx.doi.org/10.1109/tkde.2015.2507132.

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23

Wan, Chunru, and Mingchun Liu. "Content-based audio retrieval with relevance feedback." Pattern Recognition Letters 27, no. 2 (January 2006): 85–92. http://dx.doi.org/10.1016/j.patrec.2005.07.005.

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24

Rao, Yunbo, Wei Liu, Bojiang Fan, Jiali Song, and Yang Yang. "A novel relevance feedback method for CBIR." World Wide Web 21, no. 6 (February 5, 2018): 1505–22. http://dx.doi.org/10.1007/s11280-017-0523-4.

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25

Vechtomova, Olga, and Murat Karamuftuoglu. "Elicitation and use of relevance feedback information." Information Processing & Management 42, no. 1 (January 2006): 191–206. http://dx.doi.org/10.1016/j.ipm.2004.10.006.

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26

Orengo, Viviane Moreira, and Christian Huyck. "Relevance feedback and cross-language information retrieval." Information Processing & Management 42, no. 5 (September 2006): 1203–17. http://dx.doi.org/10.1016/j.ipm.2005.12.003.

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27

Franco, Annalisa, and Alessandra Lumini. "Mixture of KL subspaces for relevance feedback." Multimedia Tools and Applications 37, no. 2 (June 27, 2007): 189–209. http://dx.doi.org/10.1007/s11042-007-0139-2.

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28

Nguyen, Giang P., and Marcel Worring. "Relevance feedback based saliency adaptation in CBIR." Multimedia Systems 10, no. 6 (May 31, 2005): 499–512. http://dx.doi.org/10.1007/s00530-005-0178-3.

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29

Chen, Lin, Lin Chun, Lin Ziyu, and Zou Quan. "Hybrid pseudo-relevance feedback for microblog retrieval." Journal of Information Science 39, no. 6 (May 23, 2013): 773–88. http://dx.doi.org/10.1177/0165551513487846.

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30

Cogalmis, Kevser Nur, Oguzhan Sagoglu, and Ahmet Bulut. "AdScope: Search Campaign Scoping Using Relevance Feedback." IEEE Intelligent Systems 32, no. 3 (May 2017): 14–20. http://dx.doi.org/10.1109/mis.2017.47.

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31

Ruthven, Ian, Mounia Lalmas, and Keith van Rijsbergen. "Incorporating user search behavior into relevance feedback." Journal of the American Society for Information Science and Technology 54, no. 6 (2003): 529–49. http://dx.doi.org/10.1002/asi.10240.

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32

Bartell, Brian T., Garrison W. Cottrell, and Richard K. Belew. "Optimizing similarity using multi-query relevance feedback." Journal of the American Society for Information Science 49, no. 8 (1998): 742–61. http://dx.doi.org/10.1002/(sici)1097-4571(199806)49:8<742::aid-asi8>3.0.co;2-h.

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33

Bartell, Brian T., Garrison W. Cottrell, and Richard K. Belew. "Optimizing similarity using multi‐query relevance feedback." Journal of the American Society for Information Science 49, no. 8 (June 1998): 742–61. http://dx.doi.org/10.1002/(sici)1097-4571(199806)49:8<742::aid-asi8>3.3.co;2-8.

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34

RUTHVEN, IAN, and MOUNIA LALMAS. "A survey on the use of relevance feedback for information access systems." Knowledge Engineering Review 18, no. 2 (June 2003): 95–145. http://dx.doi.org/10.1017/s0269888903000638.

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Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems.
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35

Braam, B., K. D. Mitchell, H. A. Koomans, and L. G. Navar. "Relevance of the tubuloglomerular feedback mechanism in pathophysiology." Journal of the American Society of Nephrology 4, no. 6 (December 1993): 1257–74. http://dx.doi.org/10.1681/asn.v461257.

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The balance between a high filtration rate and high reabsorption rate in the kidney is critical in the maintenance of extracellular fluid volume. One of the mechanisms that maintain this balance is the tubuloglomerular feedback (TGF) mechanism, which operates at the level of the macula densa assessing the load and/or solute concentration coming out of the loop of Henle and controlling this load by adjusting the GFR. This review discusses the potential role of the TGF system with respect to volume homeostasis in various conditions where GFR is maintained, decreased, or increased. In most of the states discussed, the TGF system seems to act appropriately regarding volume control; however, trade-off effects occasionally occur. After acetazolamide administration, during extracellular fluid volume contraction or expansion or acute hyperkalemia, the TGF mechanism responds appropriately with regard to volume balance. After a large reduction of renal mass, the system adjusts to function at a higher level of GFR and distal delivery. In chloride-depletion metabolic alkalosis, glomerulonephritis, diabetes mellitus, and acute renal failure, the adaptation of the TGF system appears to be appropriate with regard to volume control; however, it may lead to trade-off effects, such as maintenance of metabolic alkalosis, glomerular hypertension and sclerosis, or depression of GFR, respectively. Because the TGF mechanism often contributes to compensatory adjustments to or development of disease, it can be appreciated that any in-depth evaluation of the mechanisms responsible for various pathophysiologic conditions should include an assessment of the potential role of the TGF mechanism.
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36

Mosquera, Pilar, Eduarda Soares, and Filomena Ribeiro. "THE RELEVANCE OF FEEDBACK ENVIRONMENT FOR JOB SATISFACTION." European Journal of Management Studies 23, no. 2 (2018): 85. http://dx.doi.org/10.5455/ejms/288977/2018.

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37

Lu, Wei. "Image Retrieval Based on Contour and Relevance Feedback." Applied Mechanics and Materials 182-183 (June 2012): 1771–75. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1771.

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In this paper an algorithm is proposed to retrieve images based on contour moment invariants of image and relevance feedback. Firstly, the contour of each query image is extracted and its contour moment invariant is computed. Then according to Euclid Distance between the query image and each image in the image database, the most similar images to the query image can be found. Finally, the relevance feedback algorithm based on support vector machine (SVM) is applied to improve retrieval precision. Experimental results show that the algorithm is more accurate and efficient to retrieve images with monotonous background and clear object and meet the invariance on shift, rotation and scale transform of objects.
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38

Chen, Zi Long, and Yang Lu. "Improving Relevance Feedback via Using Support Vector Machines." Advanced Materials Research 255-260 (May 2011): 2028–32. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2028.

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Traditional relevance feedback technique could help improve retrieval performance. It usually utilize the most frequent terms in the relevant documents to enrich the user’s initial query. We re-examine this method and find that many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval. This paper introduces a Support Vector Machines Based method to improve the retrieval results. Firstly, the classifier is trained on the feedback documents. Then, we can utilize this classifier to classify the rest of the documents and move relevant documents to the front of irrelevant documents. This new approach avoids modifying the initial query, so it’s a new direction for the relevance feedback techniques. Our Experiments on TREC dataset demonstrate that retrieval effectiveness can be improved more than 24.37% when our proposed approach is used.
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39

Akuma, Stephen. "Eye Gaze Relevance Feedback Indicators for Information Retrieval." International Journal of Intelligent Systems and Applications 14, no. 1 (February 8, 2022): 57–65. http://dx.doi.org/10.5815/ijisa.2022.01.05.

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There is a growing interest in the research on interactive information retrieval, particularly in the study of eye gaze-enhanced interaction. Feedback generated from user gaze features is important for developing an interactive information retrieval system. Generating these gaze features have become less difficult with the advancement of the eye tracker system over the years. In this work, eye movement as a source of relevant feedback was examined. A controlled user experiment was carried out and a set of documents were given to users to read before an eye tracker and rate the documents according to how relevant they are to a given task. Gaze features such as fixation duration, fixation count and heat maps were captured. The result showed a medium linear relationship between fixation count and user explicit ratings. Further analysis was carried out and three classifiers were compared in terms of predicting document relevance based on gaze features. It was found that the J48 decision tree classifier produced the highest accuracy.
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40

Zhong Minjuan, and Wan Changxuan. "Pseudo-Relevance Feedback Driven for XML Query Expansion." Journal of Convergence Information Technology 5, no. 9 (November 30, 2010): 146–56. http://dx.doi.org/10.4156/jcit.vol5.issue9.15.

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41

Shui-Li, Zhang, Dong Jun-Tang, and Liu Li-Li. "A Relevance Feedback Algorithm Combining Bayesian and FSRM." Open Cybernetics & Systemics Journal 9, no. 1 (May 29, 2015): 491–95. http://dx.doi.org/10.2174/1874110x01509010491.

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42

LI, JING, and YUAN YUAN. "KERNEL GBDA FOR RELEVANCE FEEDBACK IN IMAGE RETRIEVAL." International Journal of Image and Graphics 07, no. 04 (October 2007): 767–76. http://dx.doi.org/10.1142/s0219467807002908.

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Relevance feedback, as a user-in-the-loop mechanism, has been widely employed to improve the performance of content-based image retrieval. Generally, in a relevance feedback algorithm, two key components are: (1) how to select a subset of effective features from a large-scale feature pool and, (2) correspondingly, how to construct a suitable dissimilarity measure. In previous work, the biased discriminant analysis (BDA) has been proposed to address these two problems during the feedback iterations. However, BDA encounters the so called small samples size problem because it has a lack of training samples. In this paper, we utilize the generalized singular value decomposition (GSVD) to significantly reduce the small samples size problem in BDA. The developed algorithm is named GSVD for BDA (GBDA). We then kernelize the GBDA to nonlinear kernel feature space. A large amount of experiments were carried out upon a large scale database, which contains 17800 images. From the experimental results, GBDA and its kernelization are demonstrated to outperform the traditional BDA-based relevance feedback approaches and their kernel extensions, respectively.
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43

Hidekazu, Yanagimoto, and Omatu Sigeru. "Interest Extraction Using Relevance Feedback with Kernel Method." IEEJ Transactions on Electronics, Information and Systems 126, no. 3 (2006): 395–400. http://dx.doi.org/10.1541/ieejeiss.126.395.

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44

Benitez, A. B., M. Beigi, and Shih-Fu Chang. "Using relevance feedback in content-based image metasearch." IEEE Internet Computing 2, no. 4 (1998): 59–69. http://dx.doi.org/10.1109/4236.707692.

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45

Yazdi, Hadi Sadoghi, Malihe Javidi, and Hamid Reza Pourreza. "SVM-based Relevance Feedback for semantic video retrieval." International Journal of Signal and Imaging Systems Engineering 2, no. 3 (2009): 99. http://dx.doi.org/10.1504/ijsise.2009.033722.

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46

Ntalianis, Klimis, Anastasios D. Doulamis, Nicolas Tsapatsoulis, and Nikolaos E. Mastorakis. "Social Relevance Feedback Based on Multimedia Content Power." IEEE Transactions on Computational Social Systems 5, no. 1 (March 2018): 109–17. http://dx.doi.org/10.1109/tcss.2017.2766250.

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47

Bjelica, Milan. "Unobtrusive relevance feedback for personalized TV program guides." IEEE Transactions on Consumer Electronics 57, no. 2 (May 2011): 658–63. http://dx.doi.org/10.1109/tce.2011.5955205.

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48

Eravci, Bahaeddin, and Hakan Ferhatosmanoglu. "Diversity based relevance feedback for time series search." Proceedings of the VLDB Endowment 7, no. 2 (October 2013): 109–20. http://dx.doi.org/10.14778/2732228.2732230.

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49

Mosbah, Mawloud, and Bachir Boucheham. "Pseudo relevance feedback based on majority voting mechanism." International Journal of Web Science 3, no. 1 (2017): 58. http://dx.doi.org/10.1504/ijws.2017.088688.

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50

Mosbah, Mawloud, and Bachir Boucheham. "Pseudo relevance feedback based on majority voting mechanism." International Journal of Web Science 3, no. 1 (2017): 58. http://dx.doi.org/10.1504/ijws.2017.10009576.

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