NDLI: Personalization of web-search using short-term browsing context

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Personalization of web-search using short-term browsing context

Content Provider	ACM Digital Library
Author	Ustinovskiy, Yury Serdyukov, Pavel
Abstract	Search and browsing activity is known to be a valuable source of information about user's search intent. It is extensively utilized by most of modern search engines to improve ranking by constructing certain ranking features as well as by personalizing search. Personalization aims at two major goals: extraction of stable preferences of a user and specification and disambiguation of the current query. The common way to approach these problems is to extract information from user's search and browsing long-term history and to utilize short-term history to determine the context of a given query. Personalization of the web search for the first queries in new search sessions of new users is more difficult due to the lack of both long- and short-term data. In this paper we study the problem of short-term personalization. To be more precise, we restrict our attention to the set of initial queries of search sessions. These, with the lack of contextual information, are known to be the most challenging for short-term personalization and are not covered by previous studies on the subject. To approach this problem in the absence of the search context, we employ short-term browsing context. We apply a widespread framework for personalization of search results based on the re-ranking approach and evaluate our methods on the large scale data. The proposed methods are shown to significantly improve non-personalized ranking of one of the major commercial search engines. To the best of our knowledge this is the first study addressing the problem of short-term personalization based on recent browsing history. We find that performance of this re-ranking approach can be reasonably predicted given a query. When we restrict the use of our method to the queries with largest expected gain, the resulting benefit of personalization increases significantly.
Starting Page	1979
Ending Page	1988
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505679
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Personalization Browsing sessions Search context Machine learning Re-ranking
Content Type	Text
Resource Type	Article

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