{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T10:39:58Z","timestamp":1710326398980},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2016,8,4]],"date-time":"2016-08-04T00:00:00Z","timestamp":1470268800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2016,9]]},"DOI":"10.1007\/s10618-016-0474-x","type":"journal-article","created":{"date-parts":[[2016,8,4]],"date-time":"2016-08-04T14:57:12Z","timestamp":1470322632000},"page":"1166-1191","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Bayesian Wishart matrix factorization"],"prefix":"10.1007","volume":"30","author":[{"given":"Cheng","family":"Luo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiongcai","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,8,4]]},"reference":[{"key":"474_CR1","doi-asserted-by":"crossref","unstructured":"Agarwal D, Chen B-C, Elango P (2010) Fast online learning through offline initialization for time-sensitive recommendation. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD\u201910), pp 703\u2013712","DOI":"10.1145\/1835804.1835894"},{"issue":"1\u20132","key":"474_CR2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1020281327116","volume":"50","author":"C Andrieu","year":"2003","unstructured":"Andrieu C, de Freitas N, Doucet A, Jordan M (2003) An introduction to MCMC for machine learning. Mach Learn 50(1\u20132):5\u201343","journal-title":"Mach Learn"},{"issue":"1","key":"474_CR3","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1002\/jae.842","volume":"21","author":"L Bauwens","year":"2006","unstructured":"Bauwens L, Laurent S, Rombouts JVK (2006) Multivariate garch models: a survey. J Appl Econom 21(1):79\u2013109","journal-title":"J Appl Econom"},{"key":"474_CR4","volume-title":"Pattern recognition and machine learning (information science and statistics)","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer-Verlag, Inc, New York"},{"key":"474_CR5","doi-asserted-by":"crossref","unstructured":"Charlin L, Ranganath R, McInerney J, Blei DM (2015) Dynamic poisson factorization. In: Proceedings of the 9th ACM conference on recommender systems (RecSys\u201915), pp 155\u2013162","DOI":"10.1145\/2792838.2800174"},{"key":"474_CR6","doi-asserted-by":"crossref","unstructured":"Chatzis S (2014) Dynamic Bayesian probabilistic matrix factorization. In: Proceedings of the 28th AAAI conference on artificial intelligence (AAAI\u201914), pp 1731\u20131737","DOI":"10.1609\/aaai.v28i1.8951"},{"key":"474_CR7","doi-asserted-by":"crossref","unstructured":"Chowdhury N, Cai X, Luo C (2015) European conference on machine learning and principles and practice of knowledge discovery in databases (ECMLPKDD\u201915), chapter BoostMF: Boosted Matrix Factorisation for Collaborative Ranking, pp 3\u201318","DOI":"10.1007\/978-3-319-23525-7_1"},{"key":"474_CR8","doi-asserted-by":"crossref","unstructured":"Chua FCT, Oentaryo RJ, Lim E-P (2013) Modeling temporal adoptions using dynamic matrix factorization. In: Proceedings of IEEE 13th international conference on data mining (ICDM\u201913), pp 91\u2013100","DOI":"10.1109\/ICDM.2013.25"},{"key":"474_CR9","doi-asserted-by":"crossref","unstructured":"Ding Y, Li X (2005) Time weight collaborative filtering. In: Proceedings of the 14th ACM international conference on information and knowledge management (CIKM\u201905), pp 485\u2013492","DOI":"10.1145\/1099554.1099689"},{"issue":"2","key":"474_CR10","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s10618-014-0343-4","volume":"29","author":"H Gao","year":"2015","unstructured":"Gao H, Tang J, Liu H (2015) Addressing the cold-start problem in location recommendation using geo-social correlations. Data Min Knowl Disc 29(2):299\u2013323","journal-title":"Data Min Knowl Disc"},{"issue":"4","key":"474_CR11","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1093\/biomet\/82.4.711","volume":"82","author":"PJ Green","year":"1995","unstructured":"Green PJ (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82(4):711\u2013732","journal-title":"Biometrika"},{"key":"474_CR12","unstructured":"James B, Stan L (2007) The netflix prize. In: Proceedings of KDD Cup, pp 3\u20136"},{"key":"474_CR13","volume-title":"Recommender systems handbook","author":"PB Kantor","year":"2009","unstructured":"Kantor PB (2009) Recommender systems handbook. Springer, New York"},{"key":"474_CR14","unstructured":"Konstan J, Riedl J (2012) Deconstructing recommender systems. http:\/\/spectrum.ieee.org\/computing\/software\/deconstructing-recommender-systems\/ . Accessed: 28 Aug 2014"},{"key":"474_CR15","doi-asserted-by":"crossref","unstructured":"Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD\u201908), pp 447\u2013456","DOI":"10.1145\/1401890.1401944"},{"key":"474_CR16","doi-asserted-by":"crossref","unstructured":"Koren Y (2009) Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD\u201909), pp 447\u2013456","DOI":"10.1145\/1557019.1557072"},{"issue":"8","key":"474_CR17","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30\u201337","journal-title":"Computer"},{"key":"474_CR18","unstructured":"Liang X, Xi C, Tzu-Kuo H, Jeff S, Jaime GC (2010) Temporal collaborative filtering with Bayesian probabilistic tensor factorization. In: Proceedings of 2010 SIAM international conference on data mining (SAM\u201910), pp 211\u2013222"},{"key":"474_CR19","doi-asserted-by":"crossref","unstructured":"Ling G, Yang H, King I, Lyu M (2012) Online learning for collaborative filtering. In: Proceedings of the 2012 international joint conference on neural networks (IJCNN\u201912), pp 1\u20138","DOI":"10.1109\/IJCNN.2012.6252670"},{"key":"474_CR20","doi-asserted-by":"crossref","unstructured":"Liu NN, Zhao M, Xiang E, Yang Q (2010) Online evolutionary collaborative filtering. In: Proceedings of the fourth ACM conference on recommender systems (RecSys\u201910), pp 95\u2013102","DOI":"10.1145\/1864708.1864729"},{"key":"474_CR21","doi-asserted-by":"crossref","unstructured":"Lu Z, Agarwal D, Dhillon IS (2009) A spatio-temporal approach to collaborative filtering. In: Proceedings of the third ACM conference on recommender systems (RecSys\u201909), pp 13\u201320","DOI":"10.1145\/1639714.1639719"},{"key":"474_CR22","doi-asserted-by":"crossref","unstructured":"Luo C, Cai X, Chowdhury N (2014) Self-training temporal dynamic collaborative filtering. In: Proceedings of the 18th Pacific-Asia Conference on knowledge discovery and data mining (PAKDD\u201914), pp 461\u2013472","DOI":"10.1007\/978-3-319-06608-0_38"},{"key":"474_CR23","doi-asserted-by":"crossref","unstructured":"Matuszyk P, Spiliopoulou M (2014) Selective forgetting for incremental matrix factorization in recommender systems, pp 204\u2013215","DOI":"10.1007\/978-3-319-11812-3_18"},{"key":"474_CR24","doi-asserted-by":"crossref","unstructured":"McAuley JJ, Leskovec J (2013) From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: Proceedings of the 22nd international conference on world wide web (WWW\u201913), pp 897\u2013908","DOI":"10.1145\/2488388.2488466"},{"key":"474_CR25","unstructured":"Murray I, Adams RP, MacKay DJC (2010) Elliptical slice sampling. In: Proceedings of the 13th international conference on artificial intelligence and statistics (AISTATS\u201910), pp 541\u2013548"},{"key":"474_CR26","doi-asserted-by":"crossref","unstructured":"Rafailidis D, Nanopoulos A (2014) Modeling the dynamics of user preferences in coupled tensor factorization. In: Proceedings of the 8th ACM conference on recommender systems (RecSys\u201914), pp 321\u2013324","DOI":"10.1145\/2645710.2645758"},{"key":"474_CR27","doi-asserted-by":"crossref","unstructured":"Salakhutdinov R, Mnih A (2008a) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th international conference on machine learning (ICML\u201908), pp 880\u2013887","DOI":"10.1145\/1390156.1390267"},{"key":"474_CR28","unstructured":"Salakhutdinov R, Mnih A (2008b) Probabilistic matrix factorization. In: Neural information processing systems 21 (NIPS\u201908)"},{"issue":"476","key":"474_CR29","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1198\/016214506000000302","volume":"101","author":"YW Teh","year":"2006","unstructured":"Teh YW, Jordan MI, Beal MJ, Blei DM (2006) Hierarchical dirichlet processes. J Am Stat Assoc 101(476):1566\u20131581","journal-title":"J Am Stat Assoc"},{"issue":"5","key":"474_CR30","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1002\/widm.1160","volume":"5","author":"Ja Vinagre","year":"2015","unstructured":"Vinagre Ja, Jorge AM, Gama J a (2015) An overview on the exploitation of time in collaborative filtering. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 5(5):195\u2013215","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"474_CR31","doi-asserted-by":"crossref","unstructured":"Wang J, Sarwar B, Sundaresan N (2011) Utilizing related products for post-purchase recommendation in e-commerce. In: Proceedings of the Fifth ACM conference on recommender systems (RecSys\u201911), pp 329\u2013332","DOI":"10.1145\/2043932.2043995"},{"key":"474_CR32","unstructured":"Wilson A, Ghahramani Z (2011) Generalised Wishart processes. In: Proceedings of the international conference on machine learning (ICML\u201911), pp 736\u2013744"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-016-0474-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-016-0474-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-016-0474-x","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T23:39:19Z","timestamp":1656977959000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-016-0474-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,4]]},"references-count":32,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2016,9]]}},"alternative-id":["474"],"URL":"https:\/\/doi.org\/10.1007\/s10618-016-0474-x","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,8,4]]}}}