  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.
