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Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\_$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>_<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma\u2013Markov chains. Otherwise, EPF<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\_$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>_<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>Social can obtain the impact of friends on social network for user\u2019 latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.<\/jats:p>","DOI":"10.1007\/s44196-021-00022-z","type":"journal-article","created":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T13:03:36Z","timestamp":1639400616000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evolutionary Social Poisson Factorizationfor Temporal Recommendation"],"prefix":"10.1007","volume":"14","author":[{"given":"ChunYan","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YongHeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanli","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,19]]},"reference":[{"key":"22_CR1","unstructured":"Gopalan, P., Hofman, J.M., Blei, D.M.: Scalable recommendation with poisson factorization. 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