Conference Paper

Query Expansion Based on a Personalized Web Search Model

Chongqing Univ., Chongqing
DOI: 10.1109/SKG.2007.83 Conference: Semantics, Knowledge and Grid, Third International Conference on
Source: IEEE Xplore


A novel query expansion algorithm is proposed in this paper. It is based on a model of personalized web search system. The new system, as a middleware between a user and a Web search engine, is set up on the client machine. It can learn a user's preference implicitly and then generate the user profile automatically. When the user inputs query keywords, more personalized expansion words are generated by the proposed algorithm, and then these words together with the query keywords are submitted to a popular search engine such as Baidu or Google. These expansion words can help a search engine retrieval information for a user according to his/her implicit search intentions. The new Web search model can make a common search engine personalized, that is, throughout personalized query expansion the search engine can return different search results to different users who input the same keywords. The experimental results show the effect and applicability of the presented work for personalized information service of a search engine.

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    • "MAF=0.63[6] Web Search Personalization Based on Browsing History by Artificial Immune System [WSPBHAIS] Vector WordNet X X Relevance 0.75[10] Evaluating the Effectiveness of Personalized Web Search [EEPWS] Vector X X X 0.89 [11] 0.22 [11] Improving Web Search by Categorization, Clustering, and Personalization [IWSCCP] Vector ODP X X 0.53 [12] 0.57 [12] Architecture of personalized web search engine Using suffix tree clustering [APWSSTC] X X X X 0.66 [28] 0.62 [28] A Novel Page Ranking algorithm for Personalize Web Search [ANPRPWS] Vector WordNet X X 8-18 [4] 0.5 [4] Query Expansion Based on a Personalized Web Search Model [QEPWSM] Vector User defined category X 0.80 [13] "

    Full-text · Conference Paper · Mar 2015
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    • "[14]. Zhengyu Zhu et al., proposed query expansion approach based on a personalized web search model [7]. Hyun-suk Hwang et al., suggested that different kinds of contextual information is collected via various sensors & other information providing services [8]. "
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    ABSTRACT: Area of mobile web search personalization is gaining demand due to relevancy of results. Generally user relevance is captured in the form of profiles. Profile generated implicitly according to user search history or explicitly using appropriate interface. We propose personalized mobile search engine that take best features of both implicit & explicit user profiles for personalization. Implicit user profile is ontology-based & multifaceted. Concepts are divided into content concepts & location concepts to understand importance of location in the personalization. GPS locations are also used for better personalization. In this paper, explicit user profiles created, deleted & edited through user interface. System evaluates relevancy of search results using Normalized Kendall Tau Distance, precision & recall. Experimental results show that effectiveness of personalization has improved.
    Full-text · Conference Paper · Aug 2014
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    • "More specifically, they select personalized query expansion terms for web search using three different desktop oriented approaches: summarizing the entire desktop data, summarizing only the desktop documents relevant to each user query, and applying natural language processing techniques to extract dispersive lexical compounds from relevant desktop resources. A novel query expansion algorithm is proposed in (Zhu, 2007). Their web search system, acting as a middleware between an IP and a web search engine, is set up on the client machine. "
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    ABSTRACT: This paper presents an approach to personalized web search that is based on augmenting query with additional keywords extracted from interested person's profile. The profile reflects interested person's interests, past searching experiences etc. It is represented by a set of weighted keywords displayed conveniently as a keyword cloud. Experiments on web search show that our search system can improve relevancy over popular search engines.
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