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Personalised Query Suggestion for Intranet Search with Temporal User Profiling

Vu, Thanh; Willis, Alistair; Kruschwitz, Udo and Song, Dawei (2017). Personalised Query Suggestion for Intranet Search with Temporal User Profiling. In: CHIIR '17: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, ACM, New York, pp. 265–268.

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Recent research has shown the usefulness of using collective user interaction data (e.g., query logs) to recommend query modification suggestions for Intranet search. However, most of the query suggestion approaches for Intranet search follow an ``one size fits all'' strategy, whereby different users who submit an identical query would get the same query suggestion list. This is problematic, as even with the same query, different users may have different topics of interest, which may change over time in response to the user's interaction with the system.

In this paper, we address the problem by proposing a personalised query suggestion framework for Intranet search. For each search session, we construct two temporal user profiles: a click user profile using the user's clicked documents and a query user profile using the user's submitted queries. We then use the two profiles to re-rank the non-personalised query suggestion list returned by a state-of-the-art query suggestion method for Intranet search. Experimental results on a large-scale query logs collection show that our personalised framework significantly improves the quality of suggested queries.

Item Type: Conference or Workshop Item
Copyright Holders: 2017 The Authors
ISBN: 1-4503-4677-4, 978-1-4503-4677-1
Extra Information: Originally presented at CHIIR 2017: ACM SIGIR Conference on Human Information Interaction and Retrieval, Oslo, Norway, 7-11 Mar 2017.
Keywords: Interactive IR; Intranet Search; Personalised Query Suggestion; Temporal User Profiles; Learning to Rank;
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 48158
Depositing User: Thanh Vu
Date Deposited: 19 Jan 2017 15:01
Last Modified: 24 Mar 2020 11:03
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