A Hybrid Trust-Based Recommender System for Online Communities of Practice
The needs for life-long learning and the rapid development of information technologies promote the development of various types of online Community of Practices (CoPs). In online CoPs, bounded rationality and metacognition are two major issues, especially when learners face information overload and...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on learning technologies 2015-10, Vol.8 (4), p.345-356 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 356 |
|---|---|
| container_issue | 4 |
| container_start_page | 345 |
| container_title | IEEE transactions on learning technologies |
| container_volume | 8 |
| creator | Zheng, Xiao-Lin Chen, Chao-Chao Hung, Jui-Long He, Wu Hong, Fu-Xing Lin, Zhen |
| description | The needs for life-long learning and the rapid development of information technologies promote the development of various types of online Community of Practices (CoPs). In online CoPs, bounded rationality and metacognition are two major issues, especially when learners face information overload and there is no knowledge authority within the learning environment. This study proposes a hybrid, trust-based recommender system to mitigate above learning issues in online CoPs. A case study was conducted using Stack Overflow data to test the recommender system. Important findings include: (1) comparing with other social community platforms, learners in online CoPs have stronger social relations and tend to interact with a smaller group of people only; (2) the hybrid algorithm can provide more accurate recommendations than celebrity-based and content-based algorithm and; (3) the proposed recommender system can facilitate the formation of personalized learning communities. |
| doi_str_mv | 10.1109/TLT.2015.2419262 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_eric_</sourceid><recordid>TN_cdi_proquest_journals_1750085836</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ericid>EJ1145000</ericid><ieee_id>7078883</ieee_id><sourcerecordid>3899049891</sourcerecordid><originalsourceid>FETCH-LOGICAL-c388t-5fe878c58e9c542bb211754d9122282149e8ab40294f13ff5c1059ff65009d183</originalsourceid><addsrcrecordid>eNpdkMFLwzAUh4soOKd3QYSAFy-deUmzJsc5pnMMJlrPpU1fIGNtZ9Ie9t-bURni6T34fe_H44uiW6ATAKqesnU2YRTEhCWg2JSdRSNQXMXAJTv_s19GV95vKZ2yVLFRtJqR5aF0tiKZ630XPxceK_KBuq1rbCp05PPgO6yJaR3ZNDvbIJmHrG9sZ9GT1pB3V-jOaryOLkyx83jzO8fR18simy_j9eb1bT5bx5pL2cXCoEylFhKVFgkrSwaQiqRSwBiTDBKFsigTylRigBsjNFChjJkKSlUFko-jx6F379rvHn2X19Zr3O2KBtve55AqzgQIxQL68A_dtr1rwneBCn1SSD4NFB0o7VrvHZp872xduEMOND_KzYPc_Cg3_5UbTu6HE3RWn_DFCiAJtTTkd0NuEfGUpzSVUnL-A-qBfDM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1750085836</pqid></control><display><type>article</type><title>A Hybrid Trust-Based Recommender System for Online Communities of Practice</title><source>IEEE Electronic Library (IEL)</source><creator>Zheng, Xiao-Lin ; Chen, Chao-Chao ; Hung, Jui-Long ; He, Wu ; Hong, Fu-Xing ; Lin, Zhen</creator><creatorcontrib>Zheng, Xiao-Lin ; Chen, Chao-Chao ; Hung, Jui-Long ; He, Wu ; Hong, Fu-Xing ; Lin, Zhen</creatorcontrib><description>The needs for life-long learning and the rapid development of information technologies promote the development of various types of online Community of Practices (CoPs). In online CoPs, bounded rationality and metacognition are two major issues, especially when learners face information overload and there is no knowledge authority within the learning environment. This study proposes a hybrid, trust-based recommender system to mitigate above learning issues in online CoPs. A case study was conducted using Stack Overflow data to test the recommender system. Important findings include: (1) comparing with other social community platforms, learners in online CoPs have stronger social relations and tend to interact with a smaller group of people only; (2) the hybrid algorithm can provide more accurate recommendations than celebrity-based and content-based algorithm and; (3) the proposed recommender system can facilitate the formation of personalized learning communities.</description><identifier>ISSN: 1939-1382</identifier><identifier>EISSN: 1939-1382</identifier><identifier>EISSN: 2372-0050</identifier><identifier>DOI: 10.1109/TLT.2015.2419262</identifier><identifier>CODEN: ITLTAT</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Case Studies ; Collaboration ; Collaborative filtering ; Communities ; Communities of Practice ; Community relations ; Comparative Analysis ; Computer Mediated Communication ; Computer Software ; CoP ; Distance learning ; Education ; Educational recommender ; Electronic Learning ; Hybrid systems ; Information Systems ; Knowledge engineering ; Learning ; Metacognition ; Online ; Online services ; Programming ; Recommender systems ; Social Networks ; Stack Overflow ; Trust (Psychology) ; Trust management ; Trust-based algorithm</subject><ispartof>IEEE transactions on learning technologies, 2015-10, Vol.8 (4), p.345-356</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Oct-Dec 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c388t-5fe878c58e9c542bb211754d9122282149e8ab40294f13ff5c1059ff65009d183</citedby><cites>FETCH-LOGICAL-c388t-5fe878c58e9c542bb211754d9122282149e8ab40294f13ff5c1059ff65009d183</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7078883$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><backlink>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=EJ1145000$$DView record in ERIC$$Hfree_for_read</backlink></links><search><creatorcontrib>Zheng, Xiao-Lin</creatorcontrib><creatorcontrib>Chen, Chao-Chao</creatorcontrib><creatorcontrib>Hung, Jui-Long</creatorcontrib><creatorcontrib>He, Wu</creatorcontrib><creatorcontrib>Hong, Fu-Xing</creatorcontrib><creatorcontrib>Lin, Zhen</creatorcontrib><title>A Hybrid Trust-Based Recommender System for Online Communities of Practice</title><title>IEEE transactions on learning technologies</title><addtitle>TLT</addtitle><description>The needs for life-long learning and the rapid development of information technologies promote the development of various types of online Community of Practices (CoPs). In online CoPs, bounded rationality and metacognition are two major issues, especially when learners face information overload and there is no knowledge authority within the learning environment. This study proposes a hybrid, trust-based recommender system to mitigate above learning issues in online CoPs. A case study was conducted using Stack Overflow data to test the recommender system. Important findings include: (1) comparing with other social community platforms, learners in online CoPs have stronger social relations and tend to interact with a smaller group of people only; (2) the hybrid algorithm can provide more accurate recommendations than celebrity-based and content-based algorithm and; (3) the proposed recommender system can facilitate the formation of personalized learning communities.</description><subject>Algorithms</subject><subject>Case Studies</subject><subject>Collaboration</subject><subject>Collaborative filtering</subject><subject>Communities</subject><subject>Communities of Practice</subject><subject>Community relations</subject><subject>Comparative Analysis</subject><subject>Computer Mediated Communication</subject><subject>Computer Software</subject><subject>CoP</subject><subject>Distance learning</subject><subject>Education</subject><subject>Educational recommender</subject><subject>Electronic Learning</subject><subject>Hybrid systems</subject><subject>Information Systems</subject><subject>Knowledge engineering</subject><subject>Learning</subject><subject>Metacognition</subject><subject>Online</subject><subject>Online services</subject><subject>Programming</subject><subject>Recommender systems</subject><subject>Social Networks</subject><subject>Stack Overflow</subject><subject>Trust (Psychology)</subject><subject>Trust management</subject><subject>Trust-based algorithm</subject><issn>1939-1382</issn><issn>1939-1382</issn><issn>2372-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNpdkMFLwzAUh4soOKd3QYSAFy-deUmzJsc5pnMMJlrPpU1fIGNtZ9Ie9t-bURni6T34fe_H44uiW6ATAKqesnU2YRTEhCWg2JSdRSNQXMXAJTv_s19GV95vKZ2yVLFRtJqR5aF0tiKZ630XPxceK_KBuq1rbCp05PPgO6yJaR3ZNDvbIJmHrG9sZ9GT1pB3V-jOaryOLkyx83jzO8fR18simy_j9eb1bT5bx5pL2cXCoEylFhKVFgkrSwaQiqRSwBiTDBKFsigTylRigBsjNFChjJkKSlUFko-jx6F379rvHn2X19Zr3O2KBtve55AqzgQIxQL68A_dtr1rwneBCn1SSD4NFB0o7VrvHZp872xduEMOND_KzYPc_Cg3_5UbTu6HE3RWn_DFCiAJtTTkd0NuEfGUpzSVUnL-A-qBfDM</recordid><startdate>20151001</startdate><enddate>20151001</enddate><creator>Zheng, Xiao-Lin</creator><creator>Chen, Chao-Chao</creator><creator>Hung, Jui-Long</creator><creator>He, Wu</creator><creator>Hong, Fu-Xing</creator><creator>Lin, Zhen</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers, Inc</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>7SW</scope><scope>BJH</scope><scope>BNH</scope><scope>BNI</scope><scope>BNJ</scope><scope>BNO</scope><scope>ERI</scope><scope>PET</scope><scope>REK</scope><scope>WWN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20151001</creationdate><title>A Hybrid Trust-Based Recommender System for Online Communities of Practice</title><author>Zheng, Xiao-Lin ; Chen, Chao-Chao ; Hung, Jui-Long ; He, Wu ; Hong, Fu-Xing ; Lin, Zhen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c388t-5fe878c58e9c542bb211754d9122282149e8ab40294f13ff5c1059ff65009d183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Case Studies</topic><topic>Collaboration</topic><topic>Collaborative filtering</topic><topic>Communities</topic><topic>Communities of Practice</topic><topic>Community relations</topic><topic>Comparative Analysis</topic><topic>Computer Mediated Communication</topic><topic>Computer Software</topic><topic>CoP</topic><topic>Distance learning</topic><topic>Education</topic><topic>Educational recommender</topic><topic>Electronic Learning</topic><topic>Hybrid systems</topic><topic>Information Systems</topic><topic>Knowledge engineering</topic><topic>Learning</topic><topic>Metacognition</topic><topic>Online</topic><topic>Online services</topic><topic>Programming</topic><topic>Recommender systems</topic><topic>Social Networks</topic><topic>Stack Overflow</topic><topic>Trust (Psychology)</topic><topic>Trust management</topic><topic>Trust-based algorithm</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zheng, Xiao-Lin</creatorcontrib><creatorcontrib>Chen, Chao-Chao</creatorcontrib><creatorcontrib>Hung, Jui-Long</creatorcontrib><creatorcontrib>He, Wu</creatorcontrib><creatorcontrib>Hong, Fu-Xing</creatorcontrib><creatorcontrib>Lin, Zhen</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>ERIC</collection><collection>ERIC (Ovid)</collection><collection>ERIC</collection><collection>ERIC</collection><collection>ERIC (Legacy Platform)</collection><collection>ERIC( SilverPlatter )</collection><collection>ERIC</collection><collection>ERIC PlusText (Legacy Platform)</collection><collection>Education Resources Information Center (ERIC)</collection><collection>ERIC</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on learning technologies</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zheng, Xiao-Lin</au><au>Chen, Chao-Chao</au><au>Hung, Jui-Long</au><au>He, Wu</au><au>Hong, Fu-Xing</au><au>Lin, Zhen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><ericid>EJ1145000</ericid><atitle>A Hybrid Trust-Based Recommender System for Online Communities of Practice</atitle><jtitle>IEEE transactions on learning technologies</jtitle><stitle>TLT</stitle><date>2015-10-01</date><risdate>2015</risdate><volume>8</volume><issue>4</issue><spage>345</spage><epage>356</epage><pages>345-356</pages><issn>1939-1382</issn><eissn>1939-1382</eissn><eissn>2372-0050</eissn><coden>ITLTAT</coden><abstract>The needs for life-long learning and the rapid development of information technologies promote the development of various types of online Community of Practices (CoPs). In online CoPs, bounded rationality and metacognition are two major issues, especially when learners face information overload and there is no knowledge authority within the learning environment. This study proposes a hybrid, trust-based recommender system to mitigate above learning issues in online CoPs. A case study was conducted using Stack Overflow data to test the recommender system. Important findings include: (1) comparing with other social community platforms, learners in online CoPs have stronger social relations and tend to interact with a smaller group of people only; (2) the hybrid algorithm can provide more accurate recommendations than celebrity-based and content-based algorithm and; (3) the proposed recommender system can facilitate the formation of personalized learning communities.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TLT.2015.2419262</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1939-1382 |
| ispartof | IEEE transactions on learning technologies, 2015-10, Vol.8 (4), p.345-356 |
| issn | 1939-1382 1939-1382 2372-0050 |
| language | eng |
| recordid | cdi_proquest_journals_1750085836 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Case Studies Collaboration Collaborative filtering Communities Communities of Practice Community relations Comparative Analysis Computer Mediated Communication Computer Software CoP Distance learning Education Educational recommender Electronic Learning Hybrid systems Information Systems Knowledge engineering Learning Metacognition Online Online services Programming Recommender systems Social Networks Stack Overflow Trust (Psychology) Trust management Trust-based algorithm |
| title | A Hybrid Trust-Based Recommender System for Online Communities of Practice |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T08%3A35%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_eric_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Hybrid%20Trust-Based%20Recommender%20System%20for%20Online%20Communities%20of%20Practice&rft.jtitle=IEEE%20transactions%20on%20learning%20technologies&rft.au=Zheng,%20Xiao-Lin&rft.date=2015-10-01&rft.volume=8&rft.issue=4&rft.spage=345&rft.epage=356&rft.pages=345-356&rft.issn=1939-1382&rft.eissn=1939-1382&rft.coden=ITLTAT&rft_id=info:doi/10.1109/TLT.2015.2419262&rft_dat=%3Cproquest_eric_%3E3899049891%3C/proquest_eric_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1750085836&rft_id=info:pmid/&rft_ericid=EJ1145000&rft_ieee_id=7078883&rfr_iscdi=true |