Definition
The combination of structured information retrieval with user profile information represents the scenario where systems search with an explicit statement of the information need – a search query – as well as a profile of a user, which can contain information about previous interactions, search history, user demographics, or other relevant information about the user’s preferences. The relation between the profile and the information need is implicit and may contain many irrelevant signals. The task of the system then is to model both the current information need and the background user preferences to derive notions of topical relevance as well as user relevance and to find the right balance between these notions to determine the optimal ranking of search results.
Historical Background
Information retrieval research has traditionally focused on locating documents that are relevant to a user’s search query – an explicit statement of that user’s underlying information need....
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Basilico J, Hofmann T. Unifying collaborative and content-based filtering. In: Proceedings of the 21st International Conference on Machine Learning; 2004. p. 9–16.
Bogers T, Van den Bosch A. Collaborative and content-based filtering for item recommendation on social bookmarking websites. In: Jannach D, Geyer W, Freyne J, Anand SS, Dugan C, Mobasher B, Kobsa A, editors, Proceedings of the ACM RecSys ’09 Workshop on Recommender Systems and the Social Web; 2009. p. 9–16.
Chavarria-Garza H. A user profile-query model for document retrieval. PhD thesis, Southern Methodist University, 1982.
Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M. Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems; 1999.
Dean-Hall A, Clarke CLA, Kamps J, Thomas P, Simon N, Voorhees EM. Overview of the TREC 2013 contextual suggestion track. In: Voorhees EM, editor, The Twenty-Second Text REtrieval Conference (TREC 2013) Notebook. National Institute for Standards and Technology. NIST Special Publication; 2014. p. 302–500.
Hafsi M, Géry M, Beigbeder M. LaHC at INEX 2014: social book search track. In: Working Notes of the Conference and Labs of the Evaluation Forum; 2014.
Huurdeman HC, Kamps J, Koolen M, van Wees J. Using collaborative filtering in social book search. In: CLEF 2012 Evaluation Labs and Workshop, Online Working Notes; 2012. p. 17–20. http://ceur-ws.org/Vol-1178/CLEF2012wn-INEX-HuurdemanEt2012.pdf
Koolen M. User reviews in the search index? That’ll never work! In: Proceedings of the 36th European Conference on IR Research; 2014. p. 323–34. http://dx.doi.org/10.1007/978-3-319-06028-6_27.
Koolen M, Kamps J, Kazai G. Social book search: comparing topical relevance judgements and book suggestions for evaluation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management; 2012. p. 185–94.
Koolen M, Kazai G, Preminger M, Doucet A. Overview of the INEX 2013 social book search track. In: Proceedings of the CLEF 2013 Evaluation Labs and Workshop; 2013.
Koolen M, Bogers T, Kamps J, Kazai G, Preminger M. Overview of the INEX 2014 social book search track. In: Working Notes for CLEF 2014 Conference. CEUR Workshop Proceedings, vol. 1180; 2014. p. 462–79. www.CEUR-WS.org
Korfhage RR, Chavarria-Garza H. Retrieval improvement by the interaction of queries and user profiles. Department of Computer Science and Engineering, Southern Methodist University, 1982.
Mahoui M, Bhargava B, Mohania M. User content mining supporting usage content for web personalization. CERIAS technical report 2001-21, Department of Computer Science, Purdue University, 2001.
Robertson S, Hull DA. The TREC-9 filtering track final report. 2000; p. 25–40.
Voorhees EM. The philosophy of information retrieval evaluation. In: Revised Papers from the Second Workshop of the Cross-Language Evaluation Forum on Evaluation of Cross-Language Information Retrieval Systems, CLEF’01. London: Springer; 2002. p. 355–70. ISBN:3-540-44042-9. http://dl.acm.org/citation.cfm?id=648264.753539
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Koolen, M., Bogers, T. (2018). Profiles and Context for Structured Text Retrieval. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80726
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