Discovering User Preference from Folksonomy
The increasing availability of socially shared media with tags annotated makes it vital for retrieval approaches to precisely detect web content topic semantic and better understand user interest. Most existing methodologies process the queries merely considering user posted keywords and retrieve me...
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creator | Xiaohui Guo Richong Zhang Jinpeng Huai Hailong Sun Xudong Liu |
description | The increasing availability of socially shared media with tags annotated makes it vital for retrieval approaches to precisely detect web content topic semantic and better understand user interest. Most existing methodologies process the queries merely considering user posted keywords and retrieve media labeled with tags that are similar to query words, while ignoring users implicit interests and preferences. This fact stimulates us to develop preference discovering models to reveal the users' latent intents. In this paper, we study the problem of finding user preference and interest from folksonomy corpus and propose a preference-topic model that exploits probabilistic graphical model and Gibbs sampling algorithm to infer the user interested latent semantic topics. The experimental results show that, with the help of the proposed model, preference topics of the web content creators can be effectively discovered. In addition, two exemplified applications are discussed briefly. |
doi_str_mv | 10.1109/SMC.2013.362 |
format | Conference Proceeding |
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In addition, two exemplified applications are discussed briefly.</description><subject>Communities</subject><subject>Folksonomy</subject><subject>Inference algorithms</subject><subject>Interest</subject><subject>Media</subject><subject>Preference Discovery</subject><subject>Probabilistic logic</subject><subject>Semantics</subject><subject>Social Tagging</subject><subject>Tagging</subject><subject>Vectors</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>1479906522</isbn><isbn>9781479906529</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotzDFLxDAUAOAoCvZONzeX7pKa95K8NKNUT4UTBT1wO9L2RarXVhIR7t876PRtnxDnoCoA5a9eHpsKFehKEx6IBRjnvSKLeCgKtM5JIGuPRAGKUHrEtxOxyPlDKVQG6kJc3gy5m384DdN7ucmcyufEkRNPHZcxzWO5mnefeZ7mcX8qjmPYZT77dyk2q9vX5l6un-4emuu1HMDZbxl6whZ617PTjlpvIXgAY7mOgeuag2u5RyQk62rfmdAqo3VUpLpoInm9FBd_78DM2680jCHtt-QQAaz-BXIYQhg</recordid><startdate>201310</startdate><enddate>201310</enddate><creator>Xiaohui Guo</creator><creator>Richong Zhang</creator><creator>Jinpeng Huai</creator><creator>Hailong Sun</creator><creator>Xudong Liu</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201310</creationdate><title>Discovering User Preference from Folksonomy</title><author>Xiaohui Guo ; Richong Zhang ; Jinpeng Huai ; Hailong Sun ; Xudong Liu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-ad62b1d7de7376b951a91145e8fae88ea7bed226265789c4ab0433f060cf4f693</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Communities</topic><topic>Folksonomy</topic><topic>Inference algorithms</topic><topic>Interest</topic><topic>Media</topic><topic>Preference Discovery</topic><topic>Probabilistic logic</topic><topic>Semantics</topic><topic>Social Tagging</topic><topic>Tagging</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Xiaohui Guo</creatorcontrib><creatorcontrib>Richong Zhang</creatorcontrib><creatorcontrib>Jinpeng Huai</creatorcontrib><creatorcontrib>Hailong Sun</creatorcontrib><creatorcontrib>Xudong Liu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xiaohui Guo</au><au>Richong Zhang</au><au>Jinpeng Huai</au><au>Hailong Sun</au><au>Xudong Liu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Discovering User Preference from Folksonomy</atitle><btitle>2013 IEEE International Conference on Systems, Man, and Cybernetics</btitle><stitle>smc</stitle><date>2013-10</date><risdate>2013</risdate><spage>2114</spage><epage>2119</epage><pages>2114-2119</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><eisbn>1479906522</eisbn><eisbn>9781479906529</eisbn><coden>IEEPAD</coden><abstract>The increasing availability of socially shared media with tags annotated makes it vital for retrieval approaches to precisely detect web content topic semantic and better understand user interest. Most existing methodologies process the queries merely considering user posted keywords and retrieve media labeled with tags that are similar to query words, while ignoring users implicit interests and preferences. This fact stimulates us to develop preference discovering models to reveal the users' latent intents. In this paper, we study the problem of finding user preference and interest from folksonomy corpus and propose a preference-topic model that exploits probabilistic graphical model and Gibbs sampling algorithm to infer the user interested latent semantic topics. The experimental results show that, with the help of the proposed model, preference topics of the web content creators can be effectively discovered. In addition, two exemplified applications are discussed briefly.</abstract><pub>IEEE</pub><doi>10.1109/SMC.2013.362</doi><tpages>6</tpages></addata></record> |
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subjects | Communities Folksonomy Inference algorithms Interest Media Preference Discovery Probabilistic logic Semantics Social Tagging Tagging Vectors |
title | Discovering User Preference from Folksonomy |
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