{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T06:04:58Z","timestamp":1760853898924},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Context in the form of meta-data has been accredited\nas an important component in cross-domain collaborative\nfiltering (CDCF). In this research paper CDCF\nconcept is used to exploit event information (context)\nfrom two UI matrices to allow the recommendation performance\nof one domain (Facebook- User-Event Matrix)\nto benefit from the information from another domain\n(Bookmyshow- Event-Tag Matrix). The model based collaborative\nfiltering approach Tensor Factorization(TF) has\nbeen used to integrate Facebook provided User-Event context\ninformation with Bookmyshow Event-Tag context information\nto recommend events. In contrast to the standard\ncollaborative tag recommendation, our CDCF approach\nuses one User-Event matrix of Facebook that takes\nanother Bookmyshow Event-Tag matrix as additional informant.\nThe proposed cross-domain based Event Recommendation\napproach is divided into three modules- i) data\ncollection which extracts the unstructured dataset from\nthe two domains Bookmyshow and social networking site\nFacebook using API\u2019s; ii) data mapping module which is\nbasically used to integrate the common knowledge\/ data\nthat can be shared between considered different domains\n(Facebook &amp; Bookmyshow). This module integrates and\nreduces the data into structured events\u2019 instances. As the\ndataset was collected from two different sites, an intersection\nof both was taken out. Therefore this module is carefully\ndesigned according to reliability of information that\nis common between two domains; iii) 3 order tensor factorization\nand Latent Dirichlet Allocation (LDA) used for\nmost preferable recommendation by less pertinent result reduction. The proposed 3 order tensor factorization is designed\nfor maximizing the mutual benefit from both the\nconsidered domains (organizer and user). Therefore providing\nthree recommendations: For organizers: 1) system\nrecommends places to conduct specific event according to\nmaximum of attendees of a particular type of event at a\nspecific location; 2) recommending target audience to organizer:\nthose who are interested to attend event on the\nbasis of past data for promotion purposes. For users: 3) recommending\nevents to users of their interest on the basis of\npast record. Our result shows significant improvement in\nreduction of less relevant data and result effectiveness is\nmeasured through recall and precision. Reduction of less\nrelevant recommendation is 64%, 72% and 63% for place\nrecommendation to organizer, target audience recommendation\nto organizer and event recommendation to user\nrespectively. The proposed tensor factorization approach\nachieved 68% precision, 15.5% recall in recommending attendees\nto organizer and 62% precision, 13.4% recall for\nevent recommendation to user.<\/jats:p>","DOI":"10.1515\/comp-2016-0011","type":"journal-article","created":{"date-parts":[[2016,10,14]],"date-time":"2016-10-14T10:01:06Z","timestamp":1476439266000},"page":"126-137","source":"Crossref","is-referenced-by-count":8,"title":["Cross-domain based Event Recommendation\nusing Tensor Factorization"],"prefix":"10.1515","volume":"6","author":[{"given":"Anuja","family":"Arora","sequence":"first","affiliation":[{"name":"CSE\/IT Department, Jaypee Institute of Information Technology , Noida , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vaibhav","family":"Taneja","sequence":"additional","affiliation":[{"name":"CSE\/IT Department, Jaypee Institute of Information Technology , Noida , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonali","family":"Parashar","sequence":"additional","affiliation":[{"name":"CSE\/IT Department, Jaypee Institute of Information Technology , Noida , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Apurva","family":"Mishra","sequence":"additional","affiliation":[{"name":"CSE\/IT Department, Jaypee Institute of Information Technology , Noida , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2016,10,14]]},"container-title":["Open Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/comp\/6\/1\/article-p126.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2016-0011\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2016-0011\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T08:57:20Z","timestamp":1651049840000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/comp-2016-0011\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,1,1]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,2,22]]},"published-print":{"date-parts":[[2016,1,1]]}},"alternative-id":["10.1515\/comp-2016-0011"],"URL":"https:\/\/doi.org\/10.1515\/comp-2016-0011","relation":{},"ISSN":["2299-1093"],"issn-type":[{"value":"2299-1093","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,1,1]]}}}