Using Probabilistic Latent Semantic Analysis for Personalized Web Search
Lecture notes in computer sciencePublished 1 January 2005
Chenxi Lin, Gui-Rong Xue, Hua-Jun Zeng, Yong Yu
Citations29
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This paper presents an approach that mines unseen factors from web logs to personalized web search, based on probabilistic latent semantic analysis, a model based technique that is used to analyze co-occurrence data.
Abstract
Web users use search engine to find useful information on the Internet. However current web search engines return answer to a query independent of specific user information need. Since web users with similar web behaviors tend to acquire similar information when they submit a same query, these unseen factors can be used to improve search result. In this paper we present an approach that mines these unseen factors from web logs to personalized web search. Our approach is based on probabilistic latent semantic analysis, a model based technique that is used to analyze co-occurrence data. Experimental results on real data collected by MSN search engine show the improvements over traditional web search.
Keywords
Computer ScienceDecision Sciences
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