Using Probabilistic Latent Semantic Analysis for Personalized Web Search
نویسندگان
چکیده
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.
منابع مشابه
Query expansion based on relevance feedback and latent semantic analysis
Web search engines are one of the most popular tools on the Internet which are widely-used by expert and novice users. Constructing an adequate query which represents the best specification of users’ information need to the search engine is an important concern of web users. Query expansion is a way to reduce this concern and increase user satisfaction. In this paper, a new method of query expa...
متن کاملMicro-blog Personalized Query Expansion Based on Latent Topic Classification
With the increasing maturity of Web2.0 technology and development of micro-blog, the number of micro-blog pages is exponentially rising. Only relying on the traditional micro-blog search engine has not met the requirements of users. Aiming at that the retrieval efficiency of the traditional micro-blog searching method cannot meet the requirements of users, inspired by probabilistic latent seman...
متن کاملAn Integrated Architecture for Personalized Query Expansion in Web Search
In this paper we present an integrated architecture to perform personalized interactive query expansion in Web search. Our approach is to extract expansion terms in a three stage cycle: 1) keyword extraction with local analysis on search results, 2) keyword extraction with a recommender system on a community of users and 3) an algorithm to personalize the final list of suggestions. Three method...
متن کاملHierarchical Fuzzy Clustering Semantics (HFCS) in Web Document for Discovering Latent Semantics
This paper discusses about the future of the World Wide Web development, called Semantic Web. Undoubtedly, Web service is one of the most important services on the Internet, which has had the greatest impact on the generalization of the Internet in human societies. Internet penetration has been an effective factor in growth of the volume of information on the Web. The massive growth of informat...
متن کاملPersonal Name Resolution of Web People Search
Disambiguating personal names in a set of documents (such as a set of web pages returned in response to a person name) is a difficult and challenging task. In this paper, we explore the extent to which the “cluster hypothesis” for this task holds (i.e., that similar documents tend to represent the same person). We explore two clustering techniques which used either (1) term based matching (sing...
متن کامل