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Enhanced information retrieval using domain-specific recommender models
Li, Wei B.; Ganguly, Debasis ; Jones, Gareth J.F.
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 topic, new to this user, 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.
Keyword(s): Information retrieval; Domain-Specific Information Retrieval; Recommender Algorithm
Publication Date:
2011
Type: Conference item
Peer-Reviewed: Yes
Language(s): English
Institution: Dublin City University
Citation(s): Li, Wei B. and Ganguly, Debasis and Jones, Gareth J.F. (2011) Enhanced information retrieval using domain-specific recommender models. In: The 3rd International Conference on the Theory of Information Retrieval (ICTIR'11), 12-14 Sept 2011, Bertinoro, Italy.
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/16495/1/Enhanced_Information_Retrieval_Using_Domain-Specific_Recommender_Models.pdf
First Indexed: 2011-10-08 05:16:44 Last Updated: 2016-11-03 05:12:06