Epidemic Trust-Based Recommender Systems
Collaborative filtering(CF) recommender systems are among the most popular approaches to solving the information overload problem in social networks by generating accurate predictions based on the ratings of similar users. Traditional CF recommenders suffer from lack of scalability while decentraliz...
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creator | Magureanu, S. Dokoohaki, N. Mokarizadeh, S. Matskin, M. |
description | Collaborative filtering(CF) recommender systems are among the most popular approaches to solving the information overload problem in social networks by generating accurate predictions based on the ratings of similar users. Traditional CF recommenders suffer from lack of scalability while decentralized CF recommenders (DHT-based, Gossip-based etc.) have promised to alleviate this problem. Thus, in this paper we propose a decentralized approach to CF recommender systems that uses the T-Man algorithm to create and maintain an overlay network that in turn would facilitate the generation of recommendations based on local information of a node. We analyse the influence of the number of rounds and neighbors on the accuracy of prediction and item coverage and we propose a new approach to inferring trust values between a user and its neighbors. Our experiment son two datasets show an improvement of prediction accuracy relative to previous approaches while using a highly scalable, decentralized paradigm. We also analyse item coverage and show that our system is able to generate predictions for significant fraction of the users, which is comparable with the centralized approaches. |
doi_str_mv | 10.1109/SocialCom-PASSAT.2012.94 |
format | Conference Proceeding |
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We also analyse item coverage and show that our system is able to generate predictions for significant fraction of the users, which is comparable with the centralized approaches.</description><subject>Accuracy</subject><subject>collaborative filtering</subject><subject>collaborative filtering recommender systems</subject><subject>decentralized CF recommenders system</subject><subject>decentralized paradigm</subject><subject>epidemic trust-based recommender systems</subject><subject>Equations</subject><subject>Measurement</subject><subject>Peer to peer computing</subject><subject>prediction accuracy</subject><subject>Prediction algorithms</subject><subject>Recommender systems</subject><subject>Social network services</subject><subject>social networking (online)</subject><subject>social networks</subject><subject>T-Man algorithm</subject><subject>trust values</subject><subject>trusted computing</subject><isbn>1467356387</isbn><isbn>9781467356381</isbn><isbn>0769548482</isbn><isbn>9780769548487</isbn><isbn>0769548482</isbn><isbn>9780769548487</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9j09LxDAUxCMi6K77Cbz06KU1_5sca91VYUGx1WtJ0xeNbm1push-ewMrnmYGfvN4g1BCcEYI1jfVYL3ZlUOfPhdVVdQZxYRmmp-gBc6lFlxxRU_RgnCZMyGZys_RKoRPjHHsS6XZBbpej76D3tuknvZhTm9NgC55ATv0PXx3MCXVIczQh0t05swuwOpPl-h1s67Lh3T7dP9YFtvUU8LmVLZMys4JJp12lloBeeu0xk5H70hOmOCMWmKMNUQpy238xFABpqMQA1ui9Hg3_MC4b5tx8r2ZDs1gfHPn34pmmN6br_mjIURxqSJ_deQ9APzTkuO4V7FfEKVUdg</recordid><startdate>201209</startdate><enddate>201209</enddate><creator>Magureanu, S.</creator><creator>Dokoohaki, N.</creator><creator>Mokarizadeh, S.</creator><creator>Matskin, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>ADTPV</scope><scope>BNKNJ</scope><scope>D8V</scope></search><sort><creationdate>201209</creationdate><title>Epidemic Trust-Based Recommender Systems</title><author>Magureanu, S. ; Dokoohaki, N. ; Mokarizadeh, S. ; Matskin, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i213t-6b366df536f9fc2c5e7bf990f92c5f17135432c1aaca188c4c106a25ead2e4c13</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>collaborative filtering</topic><topic>collaborative filtering recommender systems</topic><topic>decentralized CF recommenders system</topic><topic>decentralized paradigm</topic><topic>epidemic trust-based recommender systems</topic><topic>Equations</topic><topic>Measurement</topic><topic>Peer to peer computing</topic><topic>prediction accuracy</topic><topic>Prediction algorithms</topic><topic>Recommender systems</topic><topic>Social network services</topic><topic>social networking (online)</topic><topic>social networks</topic><topic>T-Man algorithm</topic><topic>trust values</topic><topic>trusted computing</topic><toplevel>online_resources</toplevel><creatorcontrib>Magureanu, S.</creatorcontrib><creatorcontrib>Dokoohaki, N.</creatorcontrib><creatorcontrib>Mokarizadeh, S.</creatorcontrib><creatorcontrib>Matskin, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection><collection>SwePub</collection><collection>SwePub Conference</collection><collection>SWEPUB Kungliga Tekniska Högskolan</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Magureanu, S.</au><au>Dokoohaki, N.</au><au>Mokarizadeh, S.</au><au>Matskin, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Epidemic Trust-Based Recommender Systems</atitle><btitle>2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing</btitle><stitle>passat-socialcom</stitle><date>2012-09</date><risdate>2012</risdate><spage>461</spage><epage>470</epage><pages>461-470</pages><isbn>1467356387</isbn><isbn>9781467356381</isbn><isbn>0769548482</isbn><isbn>9780769548487</isbn><eisbn>0769548482</eisbn><eisbn>9780769548487</eisbn><coden>IEEPAD</coden><abstract>Collaborative filtering(CF) recommender systems are among the most popular approaches to solving the information overload problem in social networks by generating accurate predictions based on the ratings of similar users. Traditional CF recommenders suffer from lack of scalability while decentralized CF recommenders (DHT-based, Gossip-based etc.) have promised to alleviate this problem. Thus, in this paper we propose a decentralized approach to CF recommender systems that uses the T-Man algorithm to create and maintain an overlay network that in turn would facilitate the generation of recommendations based on local information of a node. We analyse the influence of the number of rounds and neighbors on the accuracy of prediction and item coverage and we propose a new approach to inferring trust values between a user and its neighbors. Our experiment son two datasets show an improvement of prediction accuracy relative to previous approaches while using a highly scalable, decentralized paradigm. We also analyse item coverage and show that our system is able to generate predictions for significant fraction of the users, which is comparable with the centralized approaches.</abstract><pub>IEEE</pub><doi>10.1109/SocialCom-PASSAT.2012.94</doi><tpages>10</tpages></addata></record> |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Accuracy collaborative filtering collaborative filtering recommender systems decentralized CF recommenders system decentralized paradigm epidemic trust-based recommender systems Equations Measurement Peer to peer computing prediction accuracy Prediction algorithms Recommender systems Social network services social networking (online) social networks T-Man algorithm trust values trusted computing |
title | Epidemic Trust-Based Recommender Systems |
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