The objective of an information retrieval (IR) system is to retrieve relevant items which meet a user information need. There is currently significant interest in personalized IR which seeks to improve IR effectiveness by incorporating a model of the user's interests. However, in some situations there may be no opportunity to learn about the interests of a specific user on a certain topic. In our work, we propose an IR approach which combines a recommender algorithm with IR methods to improve retrieval for domains where the system has no opportunity to learn prior information about the user's knowledge of a domain for which they have not previously entered a query. We use search data from other previous users interested in the same topic to build a recommender model for this topic. When a user enters a query on a new topic, an appropriate recommender model is selected and used to predict a ranking which the user may find interesting based on the behaviour of previous users with similar queries. The recommender output is integrated with a standard IR method in a weighted linear combination to provide a final result for the user. Experiments using the INEX 2009 data collection with a simulated recommender training set show that our approach can improve on a baseline IR system. © 2011 Springer-Verlag.
CITATION STYLE
Li, W., Ganguly, D., & Jones, G. J. F. (2011). Enhanced information retrieval using domain-specific recommender models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6931 LNCS, pp. 201–212). https://doi.org/10.1007/978-3-642-23318-0_19
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