Multi-view random walk framework for search task discovery from click-through log

  • Authors:

  • Jianwei Cui;Hongyan Liu;Jun Yan;Lei Ji;Ruoming Jin;Jun He;Yingqin Gu;Zheng Chen;Xiaoyong Du

  • Affiliations:

  • Renmin University of China, Beijing, China;Tsinghua University, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Kent State University, Ohio, USA;Renmin University of China, Beijing, China;Renmin University of China, Beijing, China;Microsoft Research Asia, Beijing, China;Renmin University of China, Beijing, China

  • Venue:
  • Proceedings of the 20th ACM international conference on Information and knowledge management
  • Year:
  • 2011

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Abstract

Search engine users often have clear search tasks hidden behind their queries. Inspired by this, the modern search engines are providing an increasing number of services to help users simplify their key tasks. However, the problem of what are the major user search tasks with high traffic for which search engines should design special services is still underexplored. In this paper, we propose a novel Multi-view Random Walk (MRW) algorithm to measure the search task oriented similarity between queries, and then group search queries with similar tasks so that the major search tasks of users can be identified from search engine click-through log. The proposed MRW, which is a general framework to combine knowledge from different views in a random walk process, allows the random surfer to walk across different views to integrate information for search task discovery. Experimental results on click-through log of a commonly used commercial search engine show that our proposed MRW algorithm can effectively discover user search tasks.