Relevant bibliographies by topics / Personalized Web search

Academic literature on the topic 'Personalized Web search'

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Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Personalized Web search"

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Choi, Dae Young. "Towards Location-Based Personalized Voice Web Search on SmartphonesTowards Location-Based Personalized Voice Web Search on Smartphones." Journal of Computers 11, no. 1 (January 2016): 62–71. http://dx.doi.org/10.17706/jcp.11.1.62-71.

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Roy T P, Roy T. P., and Ginnu George. "A Novel Personalized Web Search with Offline Capability." International Journal of Scientific Research 3, no. 5 (June 1, 2012): 243–45. http://dx.doi.org/10.15373/22778179/may2014/73.

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Sukanya, L., and R. Vijaya. "A Framework for Privacy-Enhancing Personalized Web Search." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2698–701. http://dx.doi.org/10.31142/ijtsrd12885.

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Gupta, Disha, and Nekita Chavhan. "Personalized Mobile Web Search Techniques." International Journal of Scientific and Engineering Research 4, no. 11 (November 20, 2014): 1193–98. http://dx.doi.org/10.14299/ijser.2013.11.001.

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Kim, Je-Min, and Young-Tack Park. "Personalized Search Service in Semantic Web." KIPS Transactions:PartB 13B, no. 5 (October 30, 2006): 533–40. http://dx.doi.org/10.3745/kipstb.2006.13b.5.533.

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Volkovich, Yana, and Nelly Litvak. "Asymptotic analysis for personalized Web search." Advances in Applied Probability 42, no. 02 (June 2010): 577–604. http://dx.doi.org/10.1017/s0001867800004201.

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PageRank with personalization is used in Web search as an importance measure for Web documents. The goal of this paper is to characterize the tail behavior of the PageRank distribution in the Web and other complex networks characterized by power laws. To this end, we model the PageRank as a solution of a stochastic equationwhere theRis are distributed asR. This equation is inspired by the original definition of the PageRank. In particular,Nmodels the number of incoming links to a page, andBstays for the user preference. Assuming thatNorBare heavy tailed, we employ the theory of regular variation to obtain the asymptotic behavior ofRunder quite general assumptions on the involved random variables. Our theoretical predictions show good agreement with experimental data.
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Tamboli, Najneen, and Sathish Kumar Penchala. "User Profile Based Personalized Web Search." International Journal of Managing Public Sector Information and Communication Technologies 7, no. 3 (September 30, 2016): 15–22. http://dx.doi.org/10.5121/ijmpict.2016.7302.

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Saxena, Nidhi, Shalini Agarwal, and Vinodini Katiyar. "Personalized Web Search using User Identity." International Journal of Computer Applications 147, no. 12 (August 16, 2016): 14–17. http://dx.doi.org/10.5120/ijca2016911267.

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Monika, R., V. Pavithra, D. Priya Dharshini, N. Vaishnavi, and S. Gowthami. "Privacy Based Personalized Web Search Engine." International Journal of Computer Trends and Technology 34, no. 3 (April 25, 2016): 122–24. http://dx.doi.org/10.14445/22312803/ijctt-v34p121.

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Volkovich, Yana, and Nelly Litvak. "Asymptotic analysis for personalized Web search." Advances in Applied Probability 42, no. 2 (June 2010): 577–604. http://dx.doi.org/10.1239/aap/1275055243.

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PageRank with personalization is used in Web search as an importance measure for Web documents. The goal of this paper is to characterize the tail behavior of the PageRank distribution in the Web and other complex networks characterized by power laws. To this end, we model the PageRank as a solution of a stochastic equationwhere theRis are distributed asR. This equation is inspired by the original definition of the PageRank. In particular,Nmodels the number of incoming links to a page, andBstays for the user preference. Assuming thatNorBare heavy tailed, we employ the theory of regular variation to obtain the asymptotic behavior ofRunder quite general assumptions on the involved random variables. Our theoretical predictions show good agreement with experimental data.
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Dissertations / Theses on the topic "Personalized Web search"

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Jiang, Hao, and 江浩. "Personalized web search re-ranking and content recommendation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/197548.

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In this thesis, I propose a method for establishing a personalized recommendation system for re-ranking web search results and recommending web contents. The method is based on personal reading interest which can be reflected by the user’s dwell time on each document or webpage. I acquire document-level dwell times via a customized web browser, or a mobile device. To obtain better precision, I also explore the possibility of tracking gaze position and facial expression, from which I can determine the attractiveness of different parts of a document. Inspired by idea of Google Knowledge Graph, I also establish a graph-based ontology to maintain a user profile to describe the user’s personal reading interest. Each node in the graph is a concept, which represents the user’s potential interest on this concept. I also use the dwell time to measure concept-level interest, which can be inferred from document-level user dwell times. The graph is generated based on the Wikipedia. According to the estimated concept-level user interest, my algorithm can estimate a user’s potential dwell time over a new document, based on which personalized webpage re-ranking can be carried out. I compare the rankings produced by my algorithm with rankings generated by popular commercial search engines and a recently proposed personalized ranking algorithm. The results clearly show the superiority of my method. I also use my personalized recommendation framework in other applications. A good example is personalized document summarization. The same knowledge graph is employed to estimate the weight of every word in a document; combining with a traditional document summarization algorithm which focused on text mining, I could generate a personalized summary which emphasize the user’s interest in the document. To deal with images and videos, I present a new image search and ranking algorithm for retrieving unannotated images by collaboratively mining online search results, which consists of online images and text search results. The online image search results are leveraged as reference examples to perform content-based image search over unannotated images. The online text search results are used to estimate individual reference images’ relevance to the search query as not all the online image search results are closely related to the query. Overall, the key contribution of my method lies in its ability to deal with unreliable online image search results through jointly mining visual and textual aspects of online search results. Through such collaborative mining, my algorithm infers the relevance of an online search result image to a text query. Once I estimate a query relevance score for each online image search result, I can selectively use query specific online search result images as reference examples for retrieving and ranking unannotated images. To explore the performance of my algorithm, I tested it both on a standard public image datasets and several modestly sized personal photo collections. I also compared the performance of my method with that of two peer methods. The results are very positive, which indicate that my algorithm is superior to existing content-based image search algorithms for retrieving and ranking unannotated images. Overall, the main advantage of my algorithm comes from its collaborative mining over online search results both in the visual and the textual domains.
published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy
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Crain, Steven P. "Personalized search and recommendation for health information resources." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45805.

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Consumers face several challenges using the Internet to fill health-related needs. (1) In many cases, they face a language gap as they look for information that is written in unfamiliar technical language. (2) Medical information in social media is of variable quality and may be appealing even when it is dangerous. (3) Discussion groups provide valuable social support for necessary lifestyle changes, but are variable in their levels of activity. (4) Finding less popular groups is tedious. We present solutions to these challenges. We use a novel adaptation of topic models to address the language gap. Conventional topic models discover a set of unrelated topics that together explain the combinations of words in a collection of documents. We add additional structure that provides relationships between topics corresponding to relationships between consumer and technical medical topics. This allows us to support search for technical information using informal consumer medical questions. We also analyze social media related to eating disorders. A third of these videos promote eating disorders and consumers are twice as engaged by these dangerous videos. We study the interactions of two communities in a photo-sharing site. There, a community that encourages recovery from eating disorders interacts with the pro-eating disorder community in an attempt to persuade them, but we found that this attempt entrenches the pro-eating disorder community more firmly in its position. We study the process by which consumers participate in discussion groups in an online diabetes community. We develop novel event history analysis techniques to identify the characteristics of groups in a diabetes community that are correlated with consumer activity. This analysis reveals that uniformly advertise the popular groups to all consumers impairs the diversity of the groups and limits their value to the community. To help consumers find interesting discussion groups, we develop a system for personalized recommendation for social connections. We extend matrix factorization techniques that are effective for product recommendation so that they become suitable for implicit power-law-distributed social ratings. We identify the best approaches for recommendation of a variety of social connections involving consumers, discussion groups and discussions.
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Gnasa, Melanie. "Congenial Web search a conceptual framework for personalized, collaborative, and social peer-to-peer retrieval /." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=981698069.

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Limbu, Dilip Kumar. "Contextual information retrieval from the WWW." Click here to access this resource online, 2008. http://hdl.handle.net/10292/450.

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Contextual information retrieval (CIR) is a critical technique for today’s search engines in terms of facilitating queries and returning relevant information. Despite its importance, little progress has been made in its application, due to the difficulty of capturing and representing contextual information about users. This thesis details the development and evaluation of the contextual SERL search, designed to tackle some of the challenges associated with CIR from the World Wide Web. The contextual SERL search utilises a rich contextual model that exploits implicit and explicit data to modify queries to more accurately reflect the user’s interests as well as to continually build the user’s contextual profile and a shared contextual knowledge base. These profiles are used to filter results from a standard search engine to improve the relevance of the pages displayed to the user. The contextual SERL search has been tested in an observational study that has captured both qualitative and quantitative data about the ability of the framework to improve the user’s web search experience. A total of 30 subjects, with different levels of search experience, participated in the observational study experiment. The results demonstrate that when the contextual profile and the shared contextual knowledge base are used, the contextual SERL search improves search effectiveness, efficiency and subjective satisfaction. The effectiveness improves as subjects have actually entered fewer queries to reach the target information in comparison to the contemporary search engine. In the case of a particularly complex search task, the efficiency improves as subjects have browsed fewer hits, visited fewer URLs, made fewer clicks and have taken less time to reach the target information when compared to the contemporary search engine. Finally, subjects have expressed a higher degree of satisfaction on the quality of contextual support when using the shared contextual knowledge base in comparison to using their contextual profile. These results suggest that integration of a user’s contextual factors and information seeking behaviours are very important for successful development of the CIR framework. It is believed that this framework and other similar projects will help provide the basis for the next generation of contextual information retrieval from the Web.
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Tifrea-Marciuska, Oana. "Personalised search for the Social Semantic Web." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:27bda5a8-2360-46ad-bcef-e72ae1ae6f52.

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Recently, the Web has been changing more and more to what is called the Social Semantic Web. As a consequence, the ranking of search results no longer depends solely on the structure of the interconnections among Web pages. In my research, I argue that such ranking can be based on user preferences from the Social Web, and on ontological background knowledge from the Semantic Web. Therefore, I combine preference representation languages with Semantic Web technologies. There is some related research in database community that had dedicated some time to integrate preferences in database queries. However, one cannot directly use the ideas from databases, as we additionally have ontological knowledge, which may introduce unknown values, so-called nulls. Therefore, I need to define the exact semantics and check their feasibility for this context. In my thesis, as a first step towards closing the gap between the Semantic Web, databases, and preferences, I introduce families of expressive extensions of Datalog± with preferences as new paradigms for query answering over ontologies. I first define the syntax and semantic of the proposed frameworks, then propose top-k query answering algorithms under user preferences in semantic data for different types of queries and preference models. Each of the proposed frameworks comes with advantages and disadvantages; therefore, I provide formal properties of my algorithms and empirical experiments on the performance and quality of my results. Furthermore, I explore the combination of my framework with uncertainty and the generalisation to the preferences of a group of users, where I analyse properties of my algorithms related with social choice theory.
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Tanudjaja, Francisco 1978. "Using web graph structures to personalize search." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86737.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 93-97).
by Francisco Tanudjaja.
M.Eng.
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Gopinathan-Leela, Ligon, and n/a. "Personalisation of web information search: an agent based approach." University of Canberra. Information Sciences & Engineering, 2005. http://erl.canberra.edu.au./public/adt-AUC20060728.120849.

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The main purpose of this research is to find an effective way to personalise information searching on the Internet using middleware search agents, namely, Personalised Search Agents (PSA). The PSA acts between users and search engines, and applies new and existing techniques to mine and exploit relevant and personalised information for users. Much research has already been done in developing personalising filters, as a middleware technique which can act between user and search engines to deliver more personalised results. These personalising filters, apply one or more of the popular techniques for search result personalisation, such as the category concept, learning from user actions and using metasearch engines. By developing the PSA, these techniques have been investigated and incorporated to create an effective middleware agent for web search personalisation. In this thesis, a conceptual model for the Personalised Search Agent is developed, implemented by developing a prototype and benchmarked the prototype against existing web search practices. System development methodology which has flexible and iterative procedures that switch between conceptual design and prototype development was adopted as the research methodology. In the conceptual model of the PSA, a multi-layer client server architecture is used by applying generalisation-specialisation features. The client and the server are structurally the same, but differ in the level of generalisation and interface. The client handles personalising information regarding one user whereas the server effectively combines the personalising information of all the clients (i.e. its users) to generate a global profile. Both client and server apply the category concept where user selected URLs are mapped against categories. The PSA learns the user relevant URLs both by requesting explicit feedback and by implicitly capturing user actions (for instance the active time spent by the user on a URL). The PSA also employs a keyword-generating algorithm, and tries different combinations of words in a user search string by effectively combining them with the relevant category values. The core functionalities of the conceptual model for the PSA, were implemented in a prototype, used to test the ideas in the real word. The result was benchmarked with the results from existing search engines to determine the efficiency of the PSA over conventional searching. A comparison of the test results revealed that the PSA is more effective and efficient in finding relevant and personalised results for individual users and possesses a unique user sense rather than the general user sense of traditional search engines. The PSA, is a novel architecture and contributes to the domain of knowledge web information searching, by delivering new ideas such as active time based user relevancy calculations, automatic generation of sensible search keyword combinations and the implementation of a multi-layer agent architecture. Moreover, the PSA has high potential for future extensions as well. Because it captures highly personalised data, data mining techniques which employ case-based reasoning make the PSA a more responsive, more accurate and more effective tool for personalised information searching.
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Seyedarabi, Faezeh. "Developing a model of teachers' web-based information searching : a study of search options and features to support personalised educational resource discovery." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/10018062/.

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This study has investigated the search options and features teachers use and prefer to have, when personalising their online search for teaching resources. This study focused on making web searching easier for UK teacher practitioners at primary, secondary and post-compulsory levels. In this study, a triangulated mixed method approach was carried out in a two phase iterative case study involving 75 teacher practitioners working in the UK educational setting. In this case study, a sequential evidence gathering method called ‘System Development Life Cycle’ (SDLC) was adapted linking findings obtained from the structured questionnaires, observations and semi-structured interviews in order to design, develop and test two versions of an experimental search tool called “PoSTech!”. This research has contributed to knowledge by offering a model of teachers’ web information needs and search behaviour. In this model twelve search options and features mostly used by teachers when personalising their search for finding online teaching resources via the revised search tool are listed, in order of popularity. A search options is selected by the teacher and features is the characteristic of an option teachers experiences. For example, search options 'Subject', ‘Age Group’, ‘Resource Type’, ‘Free and/ Paid resources’, ‘Search results language’, and search features that ‘Store search options selected by individual teachers and their returned results’. Teachers’ model of web information needs and search behaviour could be used by the Government, teacher trainers and search engine designers to gain an insight into the information needs and search behaviours of teachers when searching for online teaching resources by means of tackling technical barriers faced by teachers, when using the internet. In conclusion, the research work presented in this thesis has provided the initial and important steps towards understanding the web searching information needs and search behaviours of individual teachers, working in the UK educational setting.
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Tsai, Jui-yu, and 蔡叡渝. "Personalized Web Search based on User’s Preference." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/aqk298.

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碩士
國立臺灣科技大學
資訊管理系
94
Web search engines help users find useful information on the World Wide Web (WWW). However, they are usually designed to serve all users, without considering the preference of individual users. The results returned from search engines are peppered with many impertinent data under most situations, and therefore users must pay a lot of effort to filter information. On the contrary, personalized web search provides customized results depending on each user’s preference, which can save a lot of time for users. In the thesis, we propose a method for personalizing web search by reranking search engine results based on the preferences of each individual user, which can greatly aid the search when facing massive amounts of data on the internet. Our method first extracts information of users’ preferences from their interested web pages without any involvement of users. When a query is submitted, we retrieve results from a search engine and cluster them into several topics using topic keyword clusters. Then rerank web pages by topic preference which is evaluated from user profile. Experimental results reveal that our personalized web search method can effectively enhance the precision rate of the retrieved results.
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Huang, Fu-Kuang, and 黃復光. "A Personalized Intelligent Search Engine Integrator on the Web." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/68683855388220767236.

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Books on the topic "Personalized Web search"

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Kant, Tanya. Making it Personal. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190905088.001.0001.

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The encounter of “personalized experiences”—targeted advertisements, tailored information feeds, and “recommended” content, among other things—is now a common and somewhat inescapable component of digital life. More often than not however, “you” the user are not primarily responsible for personalizing your web engagements: instead, with the help of your search, browsing, and purchase histories, your “likes,” your click-throughs, and a multitude of other data you produce as you go about your day, your experience can “conveniently”—and computationally—be personalized on your behalf. This book explores a host of new questions that emerge from web users’ encounters with these forms of algorithmic personalization. What do users “know” about the algorithms that apparently “know” them? If personalization practices seek to act on users’ behalf (for instance, by deciding what content is personally relevant), then how do users retain or relinquish their autonomy? Indeed, what kinds of selfhoods are made possible when personalization algorithms intervene in identity construction? Making It Personal is the first full-length monograph to critically analyze the sociocultural implications of algorithmic personalization through the accounts and testimonies of web users themselves. At the heart of the book are interviews and focus groups with web users who—through a myriad of resistant, tactical, resigned, or trusting engagements—encounter algorithmic personalization as part of their lived experience on the web. The book proposes that for those who encounter it, algorithmic personalization creates new implications for knowledge production, autonomy, cultural capital, and formations of self.
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Volk, Hans-Dieter, and Levent Akyüz. Immunotherapy in critical illness. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0055.

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Immunotherapy in critically-ill patients is only feasible at clinical experimental level; no therapy has been approved so far. To develop a potential therapeutic strategy we need to know the pathogen, immune status of the patient, and interaction between the particular pathogen and immune cells to readjust the patient´s individually imbalanced immunological responsiveness. Giving the right treatment at the right time is crucial for a better outcome and the best economic use of resources. The process starts by matching the therapeutic selection to the clinical need. Personalized immunotherapy, highly dependent on the available biomarker, is required. Future studies on new immunotherapeutic approaches in critically-ill patients can only be interpreted in combination with immunological biomarker analyses. Immune modulation is a promising approach despite many disappointing results and there is a clear need for immunological stratification of critically-ill patients for improved efficacy. The search continues for new clinical endpoints in surviving patients with medical and health-economical impact.
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Book chapters on the topic "Personalized Web search"

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Wen, Ji-Rong, Zhicheng Dou, and Ruihua Song. "Personalized Web Search." In Encyclopedia of Database Systems, 2748–53. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_267.

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Desu, Hema Sai, Phalani Paladugu, Santoshi Sameera Adibhatla, Sushma Swaroopa Sorda, and K. S. Sudeep. "Personalized Web Search." In Advances in Intelligent Systems and Computing, 201–11. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1483-8_18.

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Wen, Ji-Rong, Zhicheng Dou, and Ruihua Song. "Personalized Web Search." In Encyclopedia of Database Systems, 2099–103. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_267.

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Wen, Ji-Rong, Zhicheng Dou, and Ruihua Song. "Personalized Web Search." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_267-2.

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Lee, Hyun Chul, and Allan Borodin. "Cluster Based Personalized Search." In Algorithms and Models for the Web-Graph, 167–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-95995-3_14.

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Biancalana, Claudio. "Social Tagging for Personalized Web Search." In AI*IA 2009: Emergent Perspectives in Artificial Intelligence, 232–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10291-2_24.

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Zhang, Jianwei, Katsutoshi Minami, Yukiko Kawai, Yuhki Shiraishi, and Tadahiko Kumamoto. "Personalized Web Search Using Emotional Features." In Availability, Reliability, and Security in Information Systems and HCI, 69–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40511-2_6.

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Wu, Yanping, Jun Zhao, Renjie Sun, Chen Chen, and Xiaoyang Wang. "Efficient Personalized Influential Community Search in Large Networks." In Web and Big Data, 86–101. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_7.

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Vishnupriya, S., B. Selvaambigai, and S. Vigneshwari. "Supporting Privacy Protection in Personalized Web Search." In Advances in Power Systems and Energy Management, 565–74. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7504-4_56.

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Yang, Dan, Tiezheng Nie, Derong Shen, Ge Yu, and Yue Kou. "Personalized Web Search with User Geographic and Temporal Preferences." In Web Technologies and Applications, 95–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20291-9_12.

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Conference papers on the topic "Personalized Web search"

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Jeh, Glen, and Jennifer Widom. "Scaling personalized web search." In the twelfth international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/775152.775191.

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Shafiq, Omair, Reda Alhajj, and Jon G. Rokne. "Community Aware Personalized Web Search." In 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2010). IEEE, 2010. http://dx.doi.org/10.1109/asonam.2010.85.

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Xu, Yabo, Ke Wang, Benyu Zhang, and Zheng Chen. "Privacy-enhancing personalized web search." In the 16th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1242572.1242652.

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Aslanyan, Grigor, Aritra Mandal, Prathyusha Senthil Kumar, Amit Jaiswal, and Manojkumar Rangasamy Kannadasan. "Personalized Ranking in eCommerce Search." In WWW '20: The Web Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3366424.3382715.

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Liang, Chunyan. "User profile for personalized web search." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019913.

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Xu, Jingqiu, Zhengyu Zhu, Xiang Ren, Yunyan Tian, and Ying Luo. "Personalized Web Search Using User Profile." In 2007 International Conference on Computational Intelligence and Security (CIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/cis.2007.167.

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Sendhilkumar, S., and T. V. Geetha. "Personalized ontology for web search personalization." In the 1st Bangalore annual Compute conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1341771.1341790.

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Leung, Kenneth Wai-Ting, Dik Lun Lee, and Wang-Chien Lee. "Personalized Web search with location preferences." In 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icde.2010.5447911.

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"A PERSONALIZED INFORMATION SEARCH ASSISTANT." In 6th International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002793200290039.

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Gupta, Jai, Zhen Qin, Michael Bendersky, and Donald Metzler. "Personalized Online Spell Correction for Personal Search." In WWW '19: The Web Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3308558.3313706.

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