{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T02:39:40Z","timestamp":1783737580732,"version":"3.55.0"},"reference-count":23,"publisher":"Tsinghua University Press","issue":"1","license":[{"start":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T00:00:00Z","timestamp":1580428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCS"],"published-print":{"date-parts":[[2020,1,31]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1\u2009M. By contrast, they have better effects on large data sets such as MovieLens 10\u2009M and MovieLens 20\u2009M.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijcs-10-2019-0030","type":"journal-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T16:14:45Z","timestamp":1580746485000},"page":"31-44","source":"Crossref","is-referenced-by-count":12,"title":["Collaborative filtering recommendation algorithm based on variational inference"],"prefix":"10.26599","volume":"4","author":[{"given":"Kai","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianjun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianghan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"11138","reference":[{"key":"ref10","article-title":"Improving variational inference with inverse autoregressive flow","author":"kingma","year":"0","journal-title":"NIPS"},{"key":"ref11","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2013","journal-title":"ArXiv Preprint"},{"key":"ref12","article-title":"BAR: Bayesian activity recognition using variational inference","author":"krishnan","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.134"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2365952.2365983"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s13735-018-0154-2"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742726"},{"key":"ref17","author":"shen","year":"2018","journal-title":"Improving variational encoder-decoders in dialogue generation"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.mechatronics.2010.09.004"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020480"},{"key":"ref4","author":"chen","year":"2016","journal-title":"Variational lossy autoencoder"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICIS.2017.7960088"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-019-09221-y"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003441"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.04.055"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_r_00310"},{"key":"ref2","article-title":"Generating sentences from a continuous space","author":"bowman","year":"2015","journal-title":"Computer Science"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"ref9","author":"hu","year":"2018","journal-title":"Learning a representation map for robot navigation using deep variational autoencoder"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783273"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835837"},{"key":"ref21","article-title":"Stochastic collapsed variational inference for hidden markov models","author":"wang","year":"2015","journal-title":"Statistics"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96890-2_13"}],"container-title":["International Journal of Crowd Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJCS-10-2019-0030\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJCS-10-2019-0030\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T14:04:53Z","timestamp":1657634693000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJCS-10-2019-0030\/full\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,31]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1,31]]}},"alternative-id":["10.1108\/IJCS-10-2019-0030"],"URL":"https:\/\/doi.org\/10.1108\/ijcs-10-2019-0030","relation":{},"ISSN":["2398-7294","2398-7294"],"issn-type":[{"value":"2398-7294","type":"print"},{"value":"2398-7294","type":"print"}],"subject":[],"published":{"date-parts":[[2020,1,31]]}}}