Learning user interests for a session-based personalized search

ABSTRACT

It is now widely assumed in personalized information retrieval (IR) area that user interests can provide substantial clues for document relevance estimation. User interests reflect generally the user background and topics of interests. However most of the proposed personalized retrieval models and strategies do not distinguish between short term and long term user interests and make use of the whole search history to improve the search accuracy. In this paper, we study how to learn long term user interests by aggregating concept-based short term ones identified within related search activities. For this purpose, we tackle the problem of session boundary recognition using context-sensitive similarity measures that are able to gauge the changes in the user interest topics with regard to reference ontology. Finally, the search personalization is achieved by re-ranking the search results for a given query using the short term user interest. Our experimental evaluation is carried out using TREC collection and shows that personalization brings significant improvements in retrieval effectiveness. Moreover, we observe that our context-sensitive session boundary recognition method can, to some extent, find a semantic correlation between the query and the user context across the search sessions.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Experimentation

Keywords:
ontology, personalization, session boundaries, user context, user interests