Clustering Query Refinements by User Intent
Venue
Proceedings of the International World Wide Web Conference (WWW) (2010)
Publication Year
2010
Authors
Eldar Sadikov, Jayant Madhavan, Lu Wang, Alon Halevy
BibTeX
Abstract
We address the problem of clustering the refinements of a user search query. The
clusters computed by our proposed algorithm can be used to improve the selection
and placement of the query suggestions proposed by a search engine, and can also
serve to summarize the different aspects of information relevant to the original
user query. Our algorithm clusters refinements based on their likely underlying user
intents by combining document click and session co-occurrence information. At its
core, our algorithm operates by performing multiple random walks on a Markov graph
that approximates user search behavior. A user study performed on top search engine
queries shows that our clusters are rated better than corresponding clusters
computed using approaches that use only document click or only sessions
co-occurrence information.
