Access concentration detection in click logs to improve mobile Web-IR

  • Authors:

  • Masaya Murata;Hiroyuki Toda;Yumiko Matsuura;Ryoji Kataoka

  • Affiliations:

  • NTT Cyber Solutions Laboratories, NTT Corporation, 1-1 Hikari-no-oka, Yokosuka-shi, Kanagawa 239-0847, Japan;NTT Cyber Solutions Laboratories, NTT Corporation, 1-1 Hikari-no-oka, Yokosuka-shi, Kanagawa 239-0847, Japan;NTT Cyber Solutions Laboratories, NTT Corporation, 1-1 Hikari-no-oka, Yokosuka-shi, Kanagawa 239-0847, Japan;NTT Cyber Solutions Laboratories, NTT Corporation, 1-1 Hikari-no-oka, Yokosuka-shi, Kanagawa 239-0847, Japan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2009

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Abstract

Effective ranking algorithms for mobile Web searches are being actively pursued. Due to the peculiar and troublesome properties of mobile contents such as scant text, few outward links, and few input keywords, conventional Web search techniques using bag-of-words ranking functions or link-based algorithms are not good enough for mobile Web searches. Our solution is to use click logs to clarify access-concentrated search results for each query and to utilize the titles and snippets to expand the queries. Many previous works regard the absolute click numbers as the degree of access concentration, but they are strongly biased such that higher-ranked search results are more easily clicked than lower-ranked ones. Therefore, it is considered that only higher-ranked search results are access-concentrated ones and that only terms extracted from them can be used to expand a query. In this paper, we introduce a new measure that is capable of estimating the degree of access concentration. This measure is used to precisely extract access concentration sites from many search results and to expand queries with terms extracted from them. We conducted an experiment using the click logs and data from an actual mobile Web search site. Results obtained show that our proposed method is a more effective way to boost the search precision than using other query expansion methods such as the top K search results or the most-often-clicked search results.