NDLI: Diversification of Web Search Results through Social Interest Mining

Content Provider	IEEE Xplore Digital Library
Author	Yuqing Mao Haifeng Shen Chengzheng Sun
Copyright Year	2012
Abstract	Traditional information retrieval techniques mainly focus on improving the relevance of result documents retrieved by a query. In most informational (or exploratory) search tasks, formulated queries may often fail to precisely capture users' real intents, and consequently retrieved results may not adequately satisfy their information needs. However, if the retrieved results were diverse in nature, some results would likely be able to meet the user's information need. In this paper, we present a novel search results diversification technique that integrates social interest mined from query logs with a probabilistic model based on query-URL bipartite graphs. A query can retrieve relevant and diverse results by incorporating the social interest, discovered through kernel principle component analysis on the related queries and URLs, with random walk on the bipartite graph. We have conducted a set of experiments to validate this technique and the results show that this technique outperforms existing results diversification techniques in terms of both the relevance and the diversity of result documents retrieved by a query.
Starting Page	3581
Ending Page	3590
File Size	596227
Page Count	10
File Format	PDF
ISBN	9781457719257
ISSN	15301605
e-ISBN	9780769545257
DOI	10.1109/HICSS.2012.224
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bipartite graph Web search Kernel Redundancy Cloud computing Cultural differences Search engines
Content Type	Text
Resource Type	Article

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