ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites
To solve the problem that users’ retrieval intentions are seldom considered by personalized websites, we propose an improved incremental collaborative filtering (CF)-based recommendation implementation method (ICFR) in this paper. The ICFR model uses one of the most popular recommendation algorithms...
Gespeichert in:
| Veröffentlicht in: | World wide web (Bussum) 2020-03, Vol.23 (2), p.1319-1340 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1340 |
|---|---|
| container_issue | 2 |
| container_start_page | 1319 |
| container_title | World wide web (Bussum) |
| container_volume | 23 |
| creator | Tang, Yayuan Guo, Kehua Zhang, Ruifang Xu, Tao Ma, Jianhua Chi, Tao |
| description | To solve the problem that users’ retrieval intentions are seldom considered by personalized websites, we propose an improved incremental collaborative filtering (CF)-based recommendation implementation method (ICFR) in this paper. The ICFR model uses one of the most popular recommendation algorithms – the collaborative filtering recommendation algorithm – for personalized websites. This paper first uses a CF algorithm to obtain the relationship between user preferences and recommended content. Second, the browsing behaviour information of users is extracted by analysing Web logs and is then converted into ratings. Finally, an incremental algorithm is designed to update historical user preference data. Based on this established model, we propose some cases for this architecture, which illustrate that the ICFR model is suitable for personalized website recommendations. |
| doi_str_mv | 10.1007/s11280-019-00693-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2228413141</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2228413141</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-ad0565c4741727aeb32d1dd390c5ed7135130c2e01c4579d4960d0574fe46a83</originalsourceid><addsrcrecordid>eNp9kF1LwzAUhoMoOKd_wKuA19V8tE3r3RhOBwNBduFdSJNT7eiaetL59evNVsE7r3Lgfd6X8BByydk1Z0zdBM5FwRLGy4SxvJTJ5xGZ8EzJhKdcHsdbFnm8s-dTchbChkVKlnxCcDlfPN3SWUehrsEOzTvQprMIW-gG01Lr29ZUHs0hqZt2AGy6F1qZAI4iWL-NpIux76hB-9oMcWWHkfVIe8DgO9M23xH-gCrENJyTk9q0AS5-3ylZL-7W84dk9Xi_nM9WiZW8HBLjWJZnNlUpV0IZqKRw3DlZMpuBU1xmXDIrgHGbZqp0aZmzWFFpDWluCjklV-Nsj_5tB2HQG7_D-JmghRBF1LJXMyVipCz6EBBq3WOzNfilOdN7tXpUq6NafVCrP2NJjqXQ72UA_k3_0_oBVqF-Vg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2228413141</pqid></control><display><type>article</type><title>ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites</title><source>SpringerLink Journals - AutoHoldings</source><creator>Tang, Yayuan ; Guo, Kehua ; Zhang, Ruifang ; Xu, Tao ; Ma, Jianhua ; Chi, Tao</creator><creatorcontrib>Tang, Yayuan ; Guo, Kehua ; Zhang, Ruifang ; Xu, Tao ; Ma, Jianhua ; Chi, Tao</creatorcontrib><description>To solve the problem that users’ retrieval intentions are seldom considered by personalized websites, we propose an improved incremental collaborative filtering (CF)-based recommendation implementation method (ICFR) in this paper. The ICFR model uses one of the most popular recommendation algorithms – the collaborative filtering recommendation algorithm – for personalized websites. This paper first uses a CF algorithm to obtain the relationship between user preferences and recommended content. Second, the browsing behaviour information of users is extracted by analysing Web logs and is then converted into ratings. Finally, an incremental algorithm is designed to update historical user preference data. Based on this established model, we propose some cases for this architecture, which illustrate that the ICFR model is suitable for personalized website recommendations.</description><identifier>ISSN: 1386-145X</identifier><identifier>EISSN: 1573-1413</identifier><identifier>DOI: 10.1007/s11280-019-00693-x</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Architecture ; Browsing ; Collaboration ; Computer Science ; Database Management ; Filtration ; Information Systems Applications (incl.Internet) ; Operating Systems ; Recommender systems ; Special Issue on Smart Computing and Cyber Technology for Cyberization ; Websites</subject><ispartof>World wide web (Bussum), 2020-03, Vol.23 (2), p.1319-1340</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>World Wide Web is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-ad0565c4741727aeb32d1dd390c5ed7135130c2e01c4579d4960d0574fe46a83</citedby><cites>FETCH-LOGICAL-c319t-ad0565c4741727aeb32d1dd390c5ed7135130c2e01c4579d4960d0574fe46a83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11280-019-00693-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11280-019-00693-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Tang, Yayuan</creatorcontrib><creatorcontrib>Guo, Kehua</creatorcontrib><creatorcontrib>Zhang, Ruifang</creatorcontrib><creatorcontrib>Xu, Tao</creatorcontrib><creatorcontrib>Ma, Jianhua</creatorcontrib><creatorcontrib>Chi, Tao</creatorcontrib><title>ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites</title><title>World wide web (Bussum)</title><addtitle>World Wide Web</addtitle><description>To solve the problem that users’ retrieval intentions are seldom considered by personalized websites, we propose an improved incremental collaborative filtering (CF)-based recommendation implementation method (ICFR) in this paper. The ICFR model uses one of the most popular recommendation algorithms – the collaborative filtering recommendation algorithm – for personalized websites. This paper first uses a CF algorithm to obtain the relationship between user preferences and recommended content. Second, the browsing behaviour information of users is extracted by analysing Web logs and is then converted into ratings. Finally, an incremental algorithm is designed to update historical user preference data. Based on this established model, we propose some cases for this architecture, which illustrate that the ICFR model is suitable for personalized website recommendations.</description><subject>Algorithms</subject><subject>Architecture</subject><subject>Browsing</subject><subject>Collaboration</subject><subject>Computer Science</subject><subject>Database Management</subject><subject>Filtration</subject><subject>Information Systems Applications (incl.Internet)</subject><subject>Operating Systems</subject><subject>Recommender systems</subject><subject>Special Issue on Smart Computing and Cyber Technology for Cyberization</subject><subject>Websites</subject><issn>1386-145X</issn><issn>1573-1413</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kF1LwzAUhoMoOKd_wKuA19V8tE3r3RhOBwNBduFdSJNT7eiaetL59evNVsE7r3Lgfd6X8BByydk1Z0zdBM5FwRLGy4SxvJTJ5xGZ8EzJhKdcHsdbFnm8s-dTchbChkVKlnxCcDlfPN3SWUehrsEOzTvQprMIW-gG01Lr29ZUHs0hqZt2AGy6F1qZAI4iWL-NpIux76hB-9oMcWWHkfVIe8DgO9M23xH-gCrENJyTk9q0AS5-3ylZL-7W84dk9Xi_nM9WiZW8HBLjWJZnNlUpV0IZqKRw3DlZMpuBU1xmXDIrgHGbZqp0aZmzWFFpDWluCjklV-Nsj_5tB2HQG7_D-JmghRBF1LJXMyVipCz6EBBq3WOzNfilOdN7tXpUq6NafVCrP2NJjqXQ72UA_k3_0_oBVqF-Vg</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Tang, Yayuan</creator><creator>Guo, Kehua</creator><creator>Zhang, Ruifang</creator><creator>Xu, Tao</creator><creator>Ma, Jianhua</creator><creator>Chi, Tao</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20200301</creationdate><title>ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites</title><author>Tang, Yayuan ; Guo, Kehua ; Zhang, Ruifang ; Xu, Tao ; Ma, Jianhua ; Chi, Tao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-ad0565c4741727aeb32d1dd390c5ed7135130c2e01c4579d4960d0574fe46a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Architecture</topic><topic>Browsing</topic><topic>Collaboration</topic><topic>Computer Science</topic><topic>Database Management</topic><topic>Filtration</topic><topic>Information Systems Applications (incl.Internet)</topic><topic>Operating Systems</topic><topic>Recommender systems</topic><topic>Special Issue on Smart Computing and Cyber Technology for Cyberization</topic><topic>Websites</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tang, Yayuan</creatorcontrib><creatorcontrib>Guo, Kehua</creatorcontrib><creatorcontrib>Zhang, Ruifang</creatorcontrib><creatorcontrib>Xu, Tao</creatorcontrib><creatorcontrib>Ma, Jianhua</creatorcontrib><creatorcontrib>Chi, Tao</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>World wide web (Bussum)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tang, Yayuan</au><au>Guo, Kehua</au><au>Zhang, Ruifang</au><au>Xu, Tao</au><au>Ma, Jianhua</au><au>Chi, Tao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites</atitle><jtitle>World wide web (Bussum)</jtitle><stitle>World Wide Web</stitle><date>2020-03-01</date><risdate>2020</risdate><volume>23</volume><issue>2</issue><spage>1319</spage><epage>1340</epage><pages>1319-1340</pages><issn>1386-145X</issn><eissn>1573-1413</eissn><abstract>To solve the problem that users’ retrieval intentions are seldom considered by personalized websites, we propose an improved incremental collaborative filtering (CF)-based recommendation implementation method (ICFR) in this paper. The ICFR model uses one of the most popular recommendation algorithms – the collaborative filtering recommendation algorithm – for personalized websites. This paper first uses a CF algorithm to obtain the relationship between user preferences and recommended content. Second, the browsing behaviour information of users is extracted by analysing Web logs and is then converted into ratings. Finally, an incremental algorithm is designed to update historical user preference data. Based on this established model, we propose some cases for this architecture, which illustrate that the ICFR model is suitable for personalized website recommendations.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11280-019-00693-x</doi><tpages>22</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1386-145X |
| ispartof | World wide web (Bussum), 2020-03, Vol.23 (2), p.1319-1340 |
| issn | 1386-145X 1573-1413 |
| language | eng |
| recordid | cdi_proquest_journals_2228413141 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Architecture Browsing Collaboration Computer Science Database Management Filtration Information Systems Applications (incl.Internet) Operating Systems Recommender systems Special Issue on Smart Computing and Cyber Technology for Cyberization Websites |
| title | ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-20T15%3A39%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=ICFR:%20An%20effective%20incremental%20collaborative%20filtering%20based%20recommendation%20architecture%20for%20personalized%20websites&rft.jtitle=World%20wide%20web%20(Bussum)&rft.au=Tang,%20Yayuan&rft.date=2020-03-01&rft.volume=23&rft.issue=2&rft.spage=1319&rft.epage=1340&rft.pages=1319-1340&rft.issn=1386-145X&rft.eissn=1573-1413&rft_id=info:doi/10.1007/s11280-019-00693-x&rft_dat=%3Cproquest_cross%3E2228413141%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2228413141&rft_id=info:pmid/&rfr_iscdi=true |