{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T22:03:07Z","timestamp":1766181787297,"version":"3.41.2"},"reference-count":33,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2022,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Multi\u2010criteria recommender systems (MCRSs) provide suggestions to users based on their preferences to various criteria. Incorporation of criteria ratings into recommendation framework can provide quality recommendations to users because these ratings can elicit users' preferences efficiently. However, elicitation of user's overall preference based on criteria ratings is a key issue in MCRS. Even though several aggregation methods for the elicitation of users' overall preference have been investigated in the literature, no method has been shown the superiority under all circumstances. Therefore, we propose a model based approach to user preference discovery in multi\u2010criteria RS using genetic programming (GP). In this work, we suggest three\u2010stage process to generate recommendations to users. First, we learn user preference transformation function to aggregate criteria ratings by using GP, and then we utilize the preference function, so derived, for computing similarities in MCRS. Finally, items are recommended to users. Experimental results on Yahoo! Movies dataset show the superiority of our proposed approach in comparison to other aggregation approaches.<\/jats:p>","DOI":"10.1002\/cpe.6899","type":"journal-article","created":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T03:16:09Z","timestamp":1645758969000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A model\u2010based approach to user preference discovery in multi\u2010criteria recommender system using genetic programming"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0909-6021","authenticated-orcid":false,"given":"Shweta","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Science &amp; Engineering The LNM Institute of Information Technology Jaipur Rajasthan 302031 India"}]},{"given":"Vibhor","family":"Kant","sequence":"additional","affiliation":[{"name":"Department of Computer Science Banaras Hindu University Varanasi Uttar Pradesh 221005 India"}]}],"member":"311","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","unstructured":"JoniS JuanC RoopeM TommiS BernardJJ. Using machine learning to predict ranking of webpages in the gift industry: factors for search\u2010engine optimization. Proceedings of the 9th International Conference on Information Systems and Technologies ICIST 2019; March 24\u201026 2019:6:1\u20106:8; ACM Cairo Egypt","DOI":"10.1145\/3361570.3361578"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"GuptaS KantV. A review and classification of multi\u2010criteria recommender systems. Proceedings of the 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS); 2020:1156\u20101162; IEEE. doi: 10.1109\/ICICCS48265.2020.9120983","DOI":"10.1109\/ICICCS48265.2020.9120983"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105756"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106771"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106585"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7637-6_25"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.03.012"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105751"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-8950-4_8"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.04.046"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.08.016"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/mis.2007.58"},{"key":"e_1_2_8_15_1","doi-asserted-by":"crossref","unstructured":"ResnickP IacovouN SuchakM BergstromP RiedlJ.GroupLens: an open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work; 1994:175\u2010186; ACM New York","DOI":"10.1145\/192844.192905"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-019-09296-3"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-4924-2"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"JannachD KarakayaZ GedikliF. Accuracy improvements for multi\u2010criteria recommender systems. Proceedings of the 13th ACM Conference on Electronic Commerce; 2012:674\u2010689; ACM","DOI":"10.1145\/2229012.2229065"},{"key":"e_1_2_8_19_1","doi-asserted-by":"crossref","unstructured":"JhalaniT KantV DwivediP. A linear regression approach to multi\u2010criteria recommender system. Proceedings of the International Conference on Data Mining and Big Data; 2016:235\u2010243; Springer.","DOI":"10.1007\/978-3-319-40973-3_23"},{"key":"e_1_2_8_20_1","doi-asserted-by":"crossref","unstructured":"PoliR LangdonWB McPheeNF KozaJR.Genetic programming: an introductory tutorial and a survey of techniques and applications. Technical report CES\u2010475; 2007:927\u20101028. doi:10.1007\/978-3-540-78293-3_22","DOI":"10.1007\/978-3-540-78293-3_22"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44874-8_4"},{"key":"e_1_2_8_22_1","doi-asserted-by":"crossref","unstructured":"GuptaS KantV.A comparative analysis of genetic programming and genetic algorithm on multi\u2010criteria recommender systems. Proceedings of the 2020 5th International Conference on Communication and Electronics Systems (ICCES); 2020:1338\u20101343; IEEE","DOI":"10.1109\/ICCES48766.2020.9138051"},{"issue":"8","key":"e_1_2_8_23_1","first-page":"126","article-title":"Genetic algorithms for feature weighting in multi\u2010criteria recommender systems","volume":"5","author":"Hwang CS","year":"2010","journal-title":"J Converg Inf Technol"},{"key":"e_1_2_8_24_1","doi-asserted-by":"crossref","unstructured":"ChoudharyP KantV DwivediP. A particle swarm optimization approach to multi criteria recommender system utilizing effective similarity measures. Proceedings of the 9th International Conference on Machine Learning and Computing; 2017:81\u201085.","DOI":"10.1145\/3055635.3056619"},{"issue":"5","key":"e_1_2_8_25_1","first-page":"24","article-title":"A multi\u2010criteria collaborative filtering recommender system using clustering and regression techniques","volume":"3","author":"Nilashi M","year":"2016","journal-title":"J Soft Comput Decis Support Syst"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.12.023"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.019"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00309-6"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.01.006"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-76312-5_90"},{"key":"e_1_2_8_31_1","doi-asserted-by":"crossref","unstructured":"JinR SiL ZhaiC CallanJ.Collaborative filtering with decoupled models for preferences and ratings. Proceedings of the 12th International Conference on Information and Knowledge Management; 2003:309\u2010316.","DOI":"10.1145\/956863.956922"},{"key":"e_1_2_8_32_1","doi-asserted-by":"crossref","unstructured":"ChongCS ZhangT LeeKK HungGG LeeBS.Collaborative analytics with genetic programming for workflow recommendation. Proceedings of the 2013 IEEE International Conference on Systems Man and Cybernetics; 2013:657\u2010662; IEEE","DOI":"10.1109\/SMC.2013.117"},{"issue":"3","key":"e_1_2_8_33_1","first-page":"295","article-title":"Guard: a genetic unified approach for recommendation","volume":"4","author":"Guimar\u00e3es A","year":"2013","journal-title":"J Inf Data Manag"},{"issue":"1","key":"e_1_2_8_34_1","first-page":"348","article-title":"Feature extraction for collaborative filtering: a genetic programming approach","volume":"9","author":"Anand D","year":"2012","journal-title":"Int J Comput Sci Issues"}],"container-title":["Concurrency and Computation: Practice and Experience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.6899","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/cpe.6899","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpe.6899","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T06:46:29Z","timestamp":1726728389000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpe.6899"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,24]]},"references-count":33,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,5,15]]}},"alternative-id":["10.1002\/cpe.6899"],"URL":"https:\/\/doi.org\/10.1002\/cpe.6899","archive":["Portico"],"relation":{},"ISSN":["1532-0626","1532-0634"],"issn-type":[{"type":"print","value":"1532-0626"},{"type":"electronic","value":"1532-0634"}],"subject":[],"published":{"date-parts":[[2022,2,24]]},"assertion":[{"value":"2021-08-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e6899"}}