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Recent research has shown the usefulness of using collective user interactiondata (e.g., query logs) to recommend query modification suggestions forIntranet search. However, most of the query suggestion approaches for Intranetsearch follow an "one size fits all" strategy, whereby different users whosubmit an identical query would get the same query suggestion list. This isproblematic, as even with the same query, different users may have differenttopics of interest, which may change over time in response to the user'sinteraction with the system. We address the problem by proposing a personalisedquery suggestion framework for Intranet search. For each search session, weconstruct two temporal user profiles: a click user profile using the user'sclicked 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 suggestionlist returned by a state-of-the-art query suggestion method for Intranetsearch. Experimental results on a large-scale query logs collection show thatour personalised framework significantly improves the quality of suggestedqueries.

Personalised Query Suggestion for Intranet Search with Temporal User Profiling