{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T12:59:31Z","timestamp":1740142771549,"version":"3.37.3"},"reference-count":71,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In this paper, we introduced two novel collaborative filtering techniques for recommendation systems in cases of various cold-start situations and incomplete datasets. The first model establishes an asymmetric weight matrix between items without using item meta-data and eradicates the disadvantages of neighborhood approaches by automatic determination of threshold values. Our first model, z-scoREC, is also regarded as a pure deep-learning model because it performs like a vanilla auto-encoder in transforming column vectors with z-score normalization similar to batch normalization. With the second model, ImposeSVD, we aimed to enhance the shortcomings of the PureSVD in cases of cold-start and incomplete data by preserving its straightforward implementation and non-parametric form. The ImposeSVD model relies on the z-scoREC, produces synthetic new predictions for the users by decomposing the latent factors from the imposed matrix. We evaluated our method on the well-known datasets and found out that our method was outperforming similar approaches in the specific scenarios including recommendations for cold-start users, strength in cold-start systems, and diversification of long-tail item recommendations in lists. Our z-scoREC model also outperformed familiar neighbor-based approaches when operated as a recommender system and gave a closer appearance to the decomposition methods despite its simple and rigid cost framework.<\/jats:p>","DOI":"10.1093\/comjnl\/bxac106","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T16:10:41Z","timestamp":1659456641000},"page":"2595-2622","source":"Crossref","is-referenced-by-count":0,"title":["ImposeSVD: Incrementing PureSVD For Top-N Recommendations for Cold-Start Problems and Sparse Datasets"],"prefix":"10.1093","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7481-8129","authenticated-orcid":false,"given":"Hakan","family":"Yilmazer","sequence":"first","affiliation":[{"name":"Department of Computer Engineering , Institute of Natural and Applied Sciences, , 01330 Balcal\u0131 Sar\u0131\u00e7am, Adana, T\u00dcRK\u0130YE"},{"name":"\u00c7ukurova University , Institute of Natural and Applied Sciences, , 01330 Balcal\u0131 Sar\u0131\u00e7am, Adana, T\u00dcRK\u0130YE"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9201-6349","authenticated-orcid":false,"given":"Selma Ay\u015fe","family":"\u00d6zel","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering , Faculty of Engineering, , 01330 Balcal\u0131 Sar\u0131\u00e7am, Adana, T\u00dcRK\u0130YE"},{"name":"\u00c7ukurova University , Faculty of Engineering, , 01330 Balcal\u0131 Sar\u0131\u00e7am, Adana, T\u00dcRK\u0130YE"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"2023111618465519800_ref1","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1145\/22899.22903","article-title":"Intelligent information-sharing systems","volume":"30","author":"Malone","year":"1987","journal-title":"Communications of the ACM"},{"key":"2023111618465519800_ref2","first-page":"175","volume-title":"Proc. of the 1994 ACM Conf. on Computer Supported Cooperative Work","author":"Resnick","year":"1994"},{"key":"2023111618465519800_ref3","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/138859.138867","article-title":"Using collaborative filtering to weave an information tapestry","volume":"35","author":"Goldberg","year":"1992","journal-title":"Communications of the ACM"},{"key":"2023111618465519800_ref4","first-page":"210","volume-title":"Proc. of the SIGCHI Conf. on Human Factors in Computing Systems","author":"Shardanand","year":"1995"},{"key":"2023111618465519800_ref5","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1145\/245108.245124","article-title":"Fab: content-based, collaborative recommendation","volume":"40","author":"Balabanovi\u0107","year":"1997","journal-title":"Communications of the ACM"},{"key":"2023111618465519800_ref6","first-page":"714","volume-title":"Aaai\/iaai","author":"Basu","year":"1998"},{"key":"2023111618465519800_ref7","article-title":"Empirical analysis of predictive algorithms for collaborative filtering","volume-title":"Proc. 14th Conf. Uncertainty in Artificial Intelligence","author":"Breese","year":"2013"},{"key":"2023111618465519800_ref8","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/963770.963772","article-title":"Evaluating collaborative filtering recommender systems","volume":"22","author":"Herlocker","year":"2004","journal-title":"ACM Trans Inf Syst"},{"key":"2023111618465519800_ref9","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"2023111618465519800_ref10","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","author":"Bobadilla","year":"2013","journal-title":"Knowledge-Based Systems"},{"key":"2023111618465519800_ref11","first-page":"285","volume-title":"Proc. of the 10th Int. Conf. on World Wide Web","author":"Sarwar","year":"2001"},{"key":"2023111618465519800_ref12","doi-asserted-by":"crossref","DOI":"10.14778\/2311906.2311916","article-title":"Challenging the long tail recommendation","volume-title":"PVLDB","author":"Yin","year":"2012"},{"volume-title":"The Long Tail: Why the Future of Business is Selling Less of More","year":"2006","author":"Anderson","key":"2023111618465519800_ref13"},{"key":"2023111618465519800_ref14","doi-asserted-by":"crossref","DOI":"10.56021\/9781421407944","volume-title":"Matrix Computations","author":"Golub","year":"2013"},{"volume-title":"ACM WebKDD Workshop","year":"2000","author":"Sarwar","key":"2023111618465519800_ref15"},{"key":"2023111618465519800_ref16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/1864708.1864721","volume-title":"Proc. of the Fourth ACM Conf. on Recommender Systems","author":"Cremonesi","year":"2010"},{"key":"2023111618465519800_ref17","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10115-018-1197-7","article-title":"EigenRec: generalizing PureSVD for effective and efficient top-N recommendations","volume":"58","author":"Nikolakopoulos","year":"2019","journal-title":"Knowledge and Information Systems"},{"key":"2023111618465519800_ref18","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/ICBK.2017.14","volume-title":"2017 IEEE Int. Conf. on Big Knowledge (ICBK)","author":"Nikolakopoulos","year":"2017"},{"key":"2023111618465519800_ref19","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1145\/3298689.3347055","volume-title":"Proc. of the 13th ACM Conf. on Recommender Systems","author":"Frolov","year":"2019"},{"volume-title":"COLLABORATIVE RECOMMENDATIONS: Algorithms, Practical Challenges and Applications","year":"2018","author":"Christakopoulou","key":"2023111618465519800_ref20"},{"key":"2023111618465519800_ref21","first-page":"659","volume-title":"Proc. of the 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining","author":"Kabbur","year":"2013"},{"key":"2023111618465519800_ref22","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/ICDM.2014.112","volume-title":"2014 IEEE Int. Conf. on Data Mining","author":"Cheng","year":"2014"},{"key":"2023111618465519800_ref23","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1145\/2835776.2835837","article-title":"Collaborative denoising auto-encoders for top-n recommender systems","volume-title":"Proc. of the Ninth ACM Int. Conf. on Web Search and Data Mining","author":"Wu","year":"2016"},{"volume-title":"Proc. of the AAAI Conf. on Artificial Intelligence","year":"2016","author":"Kang","key":"2023111618465519800_ref24"},{"key":"2023111618465519800_ref25","article-title":"The advantage of careful imputation sources in sparse data-environment of recommender systems: generating improved svd-based recommendations","volume":"37","author":"Ghazanfar","year":"2013","journal-title":"Inf. Dent."},{"key":"2023111618465519800_ref26","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.elerap.2009.08.004","article-title":"Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation","volume":"9","author":"Kim","year":"2010","journal-title":"Electronic Commerce Research and Applications"},{"key":"2023111618465519800_ref27","first-page":"550","volume-title":"Proc. of the 27th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval","author":"Basilico","year":"2004"},{"key":"2023111618465519800_ref28","first-page":"113","volume-title":"2008 20th IEEE Int. Conf. on Tools with Artificial Intelligence","author":"Weng","year":"2008"},{"key":"2023111618465519800_ref29","first-page":"593","volume-title":"WEBIST","author":"Loh","year":"2009"},{"key":"2023111618465519800_ref30","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ins.2007.07.024","article-title":"A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem","volume":"178","author":"Ahn","year":"2008","journal-title":"Inform. Sci."},{"key":"2023111618465519800_ref31","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1023\/A:1006544522159","article-title":"A framework for collaborative, content-based, and demographic filtering","volume":"13","author":"Pazzani","year":"1999","journal-title":"Artificial Intelligence Review"},{"key":"2023111618465519800_ref32","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1145\/1864708.1864736","volume-title":"Proc. of the Fourth ACM Conf. on Recommender Systems","author":"Jamali","year":"2010"},{"key":"2023111618465519800_ref33","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/978-1-84800-356-9_10","volume-title":"Computing With Social Trust","author":"Massa","year":"2009"},{"key":"2023111618465519800_ref34","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.knosys.2010.03.009","article-title":"A new collaborative filtering metric that improves the behavior of recommender systems","volume":"23","author":"Bobadilla","year":"2010","journal-title":"Knowledge-Based Systems"},{"key":"2023111618465519800_ref35","first-page":"242","volume-title":"Industrial Conf. on Data Mining","author":"Candillier","year":"2008"},{"key":"2023111618465519800_ref36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/978-1-4899-7637-6_2","volume-title":"Recommender Systems Handbook","author":"Ning","year":"2015"},{"key":"2023111618465519800_ref37","first-page":"253","volume-title":"Proc. of the 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval","author":"Schein","year":"2002"},{"key":"2023111618465519800_ref38","first-page":"238","volume-title":"2011 Int. Conf. on Innovations in Information Technology","author":"Embarak","year":"2011"},{"key":"2023111618465519800_ref39","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1145\/1639714.1639720","volume-title":"Proc. of the Third ACM Conf. on Recommender Systems","author":"Park","year":"2009"},{"key":"2023111618465519800_ref40","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.is.2014.10.001","article-title":"Dealing with the new user cold-start problem in recommender systems: a comparative review","volume":"58","author":"Son","year":"2016","journal-title":"Information Systems"},{"key":"2023111618465519800_ref41","first-page":"451","volume-title":"Proc. of the 7th ACM Conf. on Recommender systems","author":"Guo","year":"2013"},{"key":"2023111618465519800_ref42","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1109\/ICDM.2006.18","volume-title":"Sixth Int. Conf. on Data Mining (ICDM'06)","author":"Fouss","year":"2006"},{"key":"2023111618465519800_ref43","first-page":"247","volume-title":"Proc. of the Tenth Int. Conf. on Information and Knowledge Management","author":"Karypis","year":"2001"},{"key":"2023111618465519800_ref44","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1145\/963770.963776","article-title":"Item-based top-n recommendation algorithms","volume":"22","author":"Deshpande","year":"2004","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"2023111618465519800_ref45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1944339.1944341","article-title":"Novelty and diversity in top-n recommendation--analysis and evaluation","volume":"10","author":"Hurley","year":"2011","journal-title":"ACM TOIT"},{"key":"2023111618465519800_ref46","article-title":"BPR: Bayesian personalized ranking from implicit feedback","volume-title":"Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence","author":"Rendle","year":"2012"},{"key":"2023111618465519800_ref47","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1109\/ICDM.2011.134","volume-title":"2011 IEEE 11th Int. Conf. on Data Mining","author":"Ning","year":"2011"},{"key":"2023111618465519800_ref48","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/978-3-319-06605-9_4","volume-title":"Pacific-Asia Conf. on Knowledge Discovery and Data Mining","author":"Christakopoulou","year":"2014"},{"key":"2023111618465519800_ref49","first-page":"811","volume-title":"Proc. of the 23rd Int. Conf. on World Wide Web","author":"Cooper","year":"2014"},{"key":"2023111618465519800_ref50","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1145\/2792838.2800180","volume-title":"Proc. of the 9th ACM Conf. on Recommender Systems","author":"Christoffel","year":"2015"},{"key":"2023111618465519800_ref51","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1145\/3289600.3291016","volume-title":"Proc. of the Twelfth ACM Int. Conf. on Web Search and Data Mining","author":"Nikolakopoulos","year":"2019"},{"key":"2023111618465519800_ref52","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1145\/3298689.3346985","volume-title":"Proc. of the 13th ACM Conf. on Recommender Systems","author":"Nikolakopoulos","year":"2019"},{"key":"2023111618465519800_ref53","first-page":"689","volume-title":"Proc. of the 2018 world wide web Conf","author":"Liang","year":"2018"},{"key":"2023111618465519800_ref54","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1145\/3336191.3371831","volume-title":"Proc. of the 13th Int. Conf. on Web Search and Data Mining","author":"Shenbin","year":"2020"},{"key":"2023111618465519800_ref55","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/3270323.3270326","volume-title":"Proc. of the 3rd Workshop on Deep Learning for Recommender Systems","author":"Chen","year":"2018"},{"key":"2023111618465519800_ref56","doi-asserted-by":"crossref","first-page":"3251","DOI":"10.1145\/3308558.3313710","volume-title":"The World Wide Web Conf","author":"Steck","year":"2019"},{"key":"2023111618465519800_ref57","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A stochastic approximation method","volume":"22","author":"Robbins","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"2023111618465519800_ref58","first-page":"337","volume-title":"Int. Conf. on Algorithmic Applications in Management","author":"Zhou","year":"2008"},{"volume-title":"Netflix Update: Try This at Home","year":"2006","author":"Funk","key":"2023111618465519800_ref59"},{"volume-title":"Proc. of KDD Cup and Workshop at SIGKDD-07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining","year":"2007","author":"Paterek","key":"2023111618465519800_ref60"},{"key":"2023111618465519800_ref61","first-page":"30","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Comput. Secur."},{"key":"2023111618465519800_ref62","first-page":"426","volume-title":"Proc. of the 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining","author":"Koren","year":"2008"},{"key":"2023111618465519800_ref63","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1145\/1557019.1557072","volume-title":"Proc. of the 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining","author":"Koren","year":"2009"},{"key":"2023111618465519800_ref64","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781316418321","volume-title":"Algorithms and Models for Network Data and Link Analysis","author":"Fouss","year":"2016"},{"key":"2023111618465519800_ref65","first-page":"1","article-title":"The movielens datasets: history and context","volume":"5","author":"Harper","year":"2015","journal-title":"ACM TIIS"},{"key":"2023111618465519800_ref66","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/978-0-387-85820-3_8","volume-title":"Recommender Systems Handbook","author":"Shani","year":"2011"},{"key":"2023111618465519800_ref67","first-page":"35","volume-title":"Proc. of KDD Cup and Workshop","author":"Bennett","year":"2007"},{"volume-title":"R2 - Yahoo! Music","year":"2014","author":"Yahoo Labs Webscope","key":"2023111618465519800_ref68"},{"key":"2023111618465519800_ref69","first-page":"22","volume-title":"Proc. of the 14th Int. Conf. on World Wide Web","author":"Ziegler","year":"2005"},{"key":"2023111618465519800_ref70","first-page":"173","volume-title":"Proc. of the 26th Int. Conf. on World Wide Web","author":"He","year":"2017"},{"author":"Turkish Science e-Infrastructure (TRUBA)","key":"2023111618465519800_ref71"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/66\/11\/2595\/53401199\/bxac106.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/66\/11\/2595\/53401199\/bxac106.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T18:48:40Z","timestamp":1700160520000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/66\/11\/2595\/6653218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,2]]},"references-count":71,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,8,2]]},"published-print":{"date-parts":[[2023,11,14]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxac106","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"type":"print","value":"0010-4620"},{"type":"electronic","value":"1460-2067"}],"subject":[],"published-other":{"date-parts":[[2023,11]]},"published":{"date-parts":[[2022,8,2]]}}}