Name
Abstract | ||
|---|---|---|
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. 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3020165.3022129 | CHIIR |
Keywords | DocType | Volume |
Interactive IR, Intranet Search, Personalised Query Suggestion, Temporal User Profiles, Learning to Rank | Conference | abs/1701.02050 |
Citations | PageRank | References |
4 | 0.39 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thanh Vu | 1 | 40 | 6.87 |
| alistair willis | 2 | 198 | 13.63 |
| Udo Kruschwitz | 3 | 387 | 55.73 |
| Dawei Song | 4 | 472 | 45.59 |