  Logs of the interactions with a search engine show that users often
reformulate their queries. Examining these reformulations shows that
recommendations that precise the focus of a query are helpful, like those based
on expansions of the original queries. But it also shows that queries that
express some topical shift with respect to the original query can help user
access more rapidly the information they need. We propose a method to identify
from the query logs of past users queries that either focus or shift the
initial query topic. This method combines various click-based, topic-based and
session based ranking strategies and uses supervised learning in order to
maximize the semantic similarities between the query and the recommendations,
while at the same diversifying them. We evaluate our method using the
query/click logs of a Japanese web search engine and we show that the
combination of the three methods proposed is significantly better than any of
them taken individually.
