Search (3 results, page 1 of 1)

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Abstract

Personalized search approaches tailor search results to users' current interests, so as to help improve the likelihood of a user finding relevant documents for their query. Previous work on personalized search focuses on using the content of the user's query and of the documents clicked to model the user's preference. In this paper we focus on a different type of signal: We investigate the use of behavioral information for the purpose of search personalization. That is, we consider clicks and dwell time for reranking an initially retrieved list of documents. In particular, we (i) investigate the impact of distributions of users and queries on document reranking; (ii) estimate the relevance of a document for a query at 2 levels, at the query-level and at the word-level, to alleviate the problem of sparseness; and (iii) perform an experimental evaluation both for users seen during the training period and for users not seen during training. For the latter, we explore the use of information from similar users who have been seen during the training period. We use the dwell time on clicked documents to estimate a document's relevance to a query, and perform Bayesian probabilistic matrix factorization to generate a relevance distribution of a document over queries. Our experiments show that: (i) for personalized ranking, behavioral information helps to improve retrieval effectiveness; and (ii) given a query, merging information inferred from behavior of a particular user and from behaviors of other users with a user-dependent adaptive weight outperforms any combination with a fixed weight.

Footnote

A preliminary version of this paper was published in the proceedings of SIGIR '14. In this extension, we (i) extend the behavioral personalization search model introduced there to deal with queries issued by new users for whom long-term search logs are unavailable; (ii) examine the impact of sparseness on the performance of our model by considering both word-level and query-level modeling, as we find that the word-document relevance matrix is less sparse than the query-document relevance matrix; (iii) investigate the effectiveness of our behavior-based reranking model with and without assuming a uniform distribution of users as users may behave differently; (iv) include more related work and provide a detailed discussion of the experimental results.

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Date

22. 1.2023 18:37:33