Abstract
Through their interaction with search engines, users provide
implicit feedback that can be used to extract useful knowledge and improve
the quality of the search process. This feedback is encoded in the
form of a query log that consists of a sequence of search actions, which
contain information about submitted queries, documents viewed, and
documents clicked by the users. In this paper, we propose characterizing
documents and queries via the information available within a query
log, with the goal of detecting either query polysemy or spam-hosts and
spam-queries, i.e., queries that shown the undesirable property of showing
a higher rate of spam pages in their list of results than other queries.
The main contribution of our paper consists of exploiting user feedback
and query-log mining to combat spam and identify query polysemy. Our
experiments attest the effectiveness of our approach for the applications
we consider.
Users
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