Experiments on query expansion for internet yellow page services using web log mining

Authors:
Yusuke Ohura;Katsumi Takahashi;Iko Pramudiono;Masaru Kitsuregawa
Affiliations:
Institute of Industrial Science, University of Tokyo, Komaba, Meguro-ku, Tokyo;Institute of Industrial Science, University of Tokyo, Komaba, Meguro-ku, Tokyo and NIT Information Sharing Platform Laboratories, Nippon Telegraph and Telephone Corporation, Midro-cho, Musashino-s ...;Institute of Industrial Science, University of Tokyo, Komaba, Meguro-ku, Tokyo;Institute of Industrial Science, University of Tokyo, Komaba, Meguro-ku, Tokyo
Venue:
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Year:
2002

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Abstract

Tremendous amount of access log data is accumulated at many web sites. Several efforts to mine the data and apply the results to support end-users or to re-design the Web site's structure have been proposed. This paper describes our trial on access logs utilization from commercial yellow page service called "iTOWNPAGE". Our initial statistical analysis reveals that many users search various categories-even non-sibling ones in the provided hierarchy - together, or finish their search without any results that match their queries. To solve these problems, we first cluster user requests from the access logs using enhanced K-means clustering algorithm and then apply them for query expansion. Our method includes two-steps expansion that 1) recommends similar categories to the request, and 2) suggests related categories although they are nonsimilar in existing category hierarchy. We also report some evaluations that show the effectiveness of the prototype system.