Recommendation Models for User Accesses to Web Pages
Predicting the next request of a user as she visits Web pages has gained importance asWeb-based activity increases. There are a number of different approaches to prediction. Markov models and their variations, collaborative filtering models, or models based on pattern recognition techniques such as...
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| creator | Gündüz, Ş. Özsu, M.T. |
| description | Predicting the next request of a user as she visits Web pages has gained importance asWeb-based activity increases. There are a number of different approaches to prediction. Markov models and their variations, collaborative filtering models, or models based on pattern recognition techniques such as sequence mining, association rule mining, clustering user sessions or user, have been found well suited for this problem. In this paper we review these techniques and also highlight two new models that we have proposed. They consider the user access patterns to the pages as well as the time spent on these pages. We report experimental studies that show that the proposed methods can achieve a better accuracy than the other approaches. |
| doi_str_mv | 10.1007/3-540-44989-2_119 |
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User interface ; Exact sciences and technology ; Implicit Rating ; Learning and adaptive systems ; Recommendation Model ; Recommender System ; Software ; Uniform Resource Locator ; User Session</subject><ispartof>Artificial Neural Networks and Neural Information Processing -- ICANN/ICONIP 2003, 2003, Vol.2714, p.1003-1010</ispartof><rights>Springer-Verlag Berlin Heidelberg 2003</rights><rights>2004 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Lecture Notes in Computer Science</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttps://ebookcentral.proquest.com/covers/3071497-l.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/3-540-44989-2_119$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/3-540-44989-2_119$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,779,780,784,789,790,793,4050,4051,27925,38255,41442,42511</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15509815$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Alpaydin, Ethem</contributor><contributor>Oja, Erkki</contributor><contributor>Xu, Lei</contributor><contributor>Kaynak, Okyay</contributor><contributor>Xu, Lei</contributor><contributor>Kaynak, Okyay</contributor><contributor>Oja, Erkki</contributor><contributor>Alpaydin, Ethem</contributor><creatorcontrib>Gündüz, Ş.</creatorcontrib><creatorcontrib>Özsu, M.T.</creatorcontrib><title>Recommendation Models for User Accesses to Web Pages</title><title>Artificial Neural Networks and Neural Information Processing -- ICANN/ICONIP 2003</title><description>Predicting the next request of a user as she visits Web pages has gained importance asWeb-based activity increases. There are a number of different approaches to prediction. Markov models and their variations, collaborative filtering models, or models based on pattern recognition techniques such as sequence mining, association rule mining, clustering user sessions or user, have been found well suited for this problem. In this paper we review these techniques and also highlight two new models that we have proposed. They consider the user access patterns to the pages as well as the time spent on these pages. We report experimental studies that show that the proposed methods can achieve a better accuracy than the other approaches.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems and distributed systems. User interface</subject><subject>Exact sciences and technology</subject><subject>Implicit Rating</subject><subject>Learning and adaptive systems</subject><subject>Recommendation Model</subject><subject>Recommender System</subject><subject>Software</subject><subject>Uniform Resource Locator</subject><subject>User Session</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540404082</isbn><isbn>9783540404088</isbn><isbn>9783540449898</isbn><isbn>3540449892</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2003</creationdate><recordtype>book_chapter</recordtype><recordid>eNotkDtPwzAQx81TlNIPwJaFMdTns2t7RBUvqQiEqBgtxzmXQpsUOwx8e9KWu-Gk_-OGH2OXwK-Bcz3GUkleSmmNLYUDsAdsZLXBXt2J5pANYAJQIkp7xM53Rr9GHLMBRy5KqyWesoFVRgkEEGdslPMn7wcFSBQDJl8ptOs1NbXvlm1TPLU1rXIR21TMM6XiJgTKmXLRtcU7VcWLX1C-YCfRrzKN_u-Qze9u36YP5ez5_nF6Mys3QvOurGLQvorkhQhKTbxGM6nqKsRIUNuIoLyvjbAxymjBIHoBoH3QsSI7MRUO2dX-78bn4Fcx-SYss9uk5dqnXwdKcWtA9bnxPpd7q1lQclXbfmUH3G1BOnQ9Gbdj5nYg-4b8_5za7x_KnaNtJVDTJb8KH37TUcoOuQZptQOhnDUS_wBzD3B6</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Gündüz, Ş.</creator><creator>Özsu, M.T.</creator><general>Springer Berlin / Heidelberg</general><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>FFUUA</scope><scope>IQODW</scope></search><sort><creationdate>2003</creationdate><title>Recommendation Models for User Accesses to Web Pages</title><author>Gündüz, Ş. ; Özsu, M.T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p270t-bfc7abfea22c556a7386bdbcffe1d9f315aad829ff4f91833a2117ac7fbe968b3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Computer systems and distributed systems. User interface</topic><topic>Exact sciences and technology</topic><topic>Implicit Rating</topic><topic>Learning and adaptive systems</topic><topic>Recommendation Model</topic><topic>Recommender System</topic><topic>Software</topic><topic>Uniform Resource Locator</topic><topic>User Session</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gündüz, Ş.</creatorcontrib><creatorcontrib>Özsu, M.T.</creatorcontrib><collection>ProQuest Ebook Central - Book Chapters - Demo use only</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gündüz, Ş.</au><au>Özsu, M.T.</au><au>Alpaydin, Ethem</au><au>Oja, Erkki</au><au>Xu, Lei</au><au>Kaynak, Okyay</au><au>Xu, Lei</au><au>Kaynak, Okyay</au><au>Oja, Erkki</au><au>Alpaydin, Ethem</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Recommendation Models for User Accesses to Web Pages</atitle><btitle>Artificial Neural Networks and Neural Information Processing -- ICANN/ICONIP 2003</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2003</date><risdate>2003</risdate><volume>2714</volume><spage>1003</spage><epage>1010</epage><pages>1003-1010</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540404082</isbn><isbn>9783540404088</isbn><eisbn>9783540449898</eisbn><eisbn>3540449892</eisbn><abstract>Predicting the next request of a user as she visits Web pages has gained importance asWeb-based activity increases. There are a number of different approaches to prediction. Markov models and their variations, collaborative filtering models, or models based on pattern recognition techniques such as sequence mining, association rule mining, clustering user sessions or user, have been found well suited for this problem. In this paper we review these techniques and also highlight two new models that we have proposed. They consider the user access patterns to the pages as well as the time spent on these pages. We report experimental studies that show that the proposed methods can achieve a better accuracy than the other approaches.</abstract><cop>Germany</cop><pub>Springer Berlin / Heidelberg</pub><doi>10.1007/3-540-44989-2_119</doi><oclcid>958523112</oclcid><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Artificial Neural Networks and Neural Information Processing -- ICANN/ICONIP 2003, 2003, Vol.2714, p.1003-1010 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_15509815 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Computer systems and distributed systems. User interface Exact sciences and technology Implicit Rating Learning and adaptive systems Recommendation Model Recommender System Software Uniform Resource Locator User Session |
| title | Recommendation Models for User Accesses to Web Pages |
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