TY - GEN
T1 - Personalised query suggestion for intranet search with temporal user profiling
AU - Vu, Thanh
AU - Willis, Alistair
AU - Kruschwitz, Udo
AU - Song, Dawei
N1 - Publisher Copyright:
© 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2017/3/7
Y1 - 2017/3/7
N2 - 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.
AB - 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.
KW - Interactive IR
KW - Intranet Search
KW - Learning to Rank
KW - Personalised Query Suggestion
KW - Temporal User Profiles
UR - http://www.scopus.com/inward/record.url?scp=85016998077&partnerID=8YFLogxK
U2 - 10.1145/3020165.3022129
DO - 10.1145/3020165.3022129
M3 - Conference contribution
AN - SCOPUS:85016998077
T3 - CHIIR 2017 - Proceedings of the 2017 Conference Human Information Interaction and Retrieval
SP - 265
EP - 268
BT - CHIIR 2017 - Proceedings of the 2017 Conference Human Information Interaction and Retrieval
PB - Association for Computing Machinery, Inc
T2 - 2nd ACM SIGIR Conference on Information Interaction and Retrieval, CHIIR 2017
Y2 - 7 March 2017 through 11 March 2017
ER -