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

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

Content Provider	ACM Digital Library
Author	Cui, Jianwei Du, Xiaoyong Gu, Yingqin Ji, Lei Jin, Ruoming Yan, Jun Chen, Zheng Liu, Hongyan He, Jun
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.
Starting Page	135
Ending Page	140
Page Count	6
File Format	PDF
ISBN	9781450307178
DOI	10.1145/2063576.2063600
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Search task Multi-view random walk Query clustering
Content Type	Text
Resource Type	Article

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