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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Xiaohui Guo, Richong Zhang, Jinpeng Huai, Hailong Sun, Xudong Liu
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 2119
container_issue
container_start_page 2114
container_title
container_volume
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
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6722115</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6722115</ieee_id><sourcerecordid>6722115</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-ad62b1d7de7376b951a91145e8fae88ea7bed226265789c4ab0433f060cf4f693</originalsourceid><addsrcrecordid>eNotzDFLxDAUAOAoCvZONzeX7pKa95K8NKNUT4UTBT1wO9L2RarXVhIR7t876PRtnxDnoCoA5a9eHpsKFehKEx6IBRjnvSKLeCgKtM5JIGuPRAGKUHrEtxOxyPlDKVQG6kJc3gy5m384DdN7ucmcyufEkRNPHZcxzWO5mnefeZ7mcX8qjmPYZT77dyk2q9vX5l6un-4emuu1HMDZbxl6whZ617PTjlpvIXgAY7mOgeuag2u5RyQk62rfmdAqo3VUpLpoInm9FBd_78DM2680jCHtt-QQAaz-BXIYQhg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Discovering User Preference from Folksonomy</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Xiaohui Guo ; Richong Zhang ; Jinpeng Huai ; Hailong Sun ; Xudong Liu</creator><creatorcontrib>Xiaohui Guo ; Richong Zhang ; Jinpeng Huai ; Hailong Sun ; Xudong Liu</creatorcontrib><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.</description><identifier>ISSN: 1062-922X</identifier><identifier>EISSN: 2577-1655</identifier><identifier>EISBN: 1479906522</identifier><identifier>EISBN: 9781479906529</identifier><identifier>DOI: 10.1109/SMC.2013.362</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>Communities ; Folksonomy ; Inference algorithms ; Interest ; Media ; Preference Discovery ; Probabilistic logic ; Semantics ; Social Tagging ; Tagging ; Vectors</subject><ispartof>2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013, p.2114-2119</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6722115$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27916,54911</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6722115$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Xiaohui Guo</creatorcontrib><creatorcontrib>Richong Zhang</creatorcontrib><creatorcontrib>Jinpeng Huai</creatorcontrib><creatorcontrib>Hailong Sun</creatorcontrib><creatorcontrib>Xudong Liu</creatorcontrib><title>Discovering User Preference from Folksonomy</title><title>2013 IEEE International Conference on Systems, Man, and Cybernetics</title><addtitle>smc</addtitle><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.</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>
fulltext fulltext_linktorsrc
identifier ISSN: 1062-922X
ispartof 2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013, p.2114-2119
issn 1062-922X
2577-1655
language eng
recordid cdi_ieee_primary_6722115
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Communities
Folksonomy
Inference algorithms
Interest
Media
Preference Discovery
Probabilistic logic
Semantics
Social Tagging
Tagging
Vectors
title Discovering User Preference from Folksonomy
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T02%3A56%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Discovering%20User%20Preference%20from%20Folksonomy&rft.btitle=2013%20IEEE%20International%20Conference%20on%20Systems,%20Man,%20and%20Cybernetics&rft.au=Xiaohui%20Guo&rft.date=2013-10&rft.spage=2114&rft.epage=2119&rft.pages=2114-2119&rft.issn=1062-922X&rft.eissn=2577-1655&rft.coden=IEEPAD&rft_id=info:doi/10.1109/SMC.2013.362&rft_dat=%3Cieee_6IE%3E6722115%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1479906522&rft.eisbn_list=9781479906529&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6722115&rfr_iscdi=true