Enhanced information retrieval using domain-specific recommender models
Li, Wei B.ORCID: 0000-0001-7347-3501, Ganguly, DebasisORCID: 0000-0003-0050-7138 and Jones, Gareth J.F.ORCID: 0000-0003-2923-8365
(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.
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.
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Domain-Specific Information Retrieval; Recommender Algorithm