{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/recurrent-poisson-factorization-for-temporal","title":"Recurrent Poisson Factorization for Temporal Recommendation","arxiv_id":"1703.01442","date":"2017-03-04","proceeding":null,"authors":["Seyed Abbas Hosseini","Keivan Alizadeh","Ali Khodadadi","Ali Arabzadeh","Mehrdad Farajtabar","Hongyuan Zha","Hamid R. Rabiee"],"abstract":"Poisson factorization is a probabilistic model of users and items for\nrecommendation systems, where the so-called implicit consumer data is modeled\nby a factorized Poisson distribution. There are many variants of Poisson\nfactorization methods who show state-of-the-art performance on real-world\nrecommendation tasks. However, most of them do not explicitly take into account\nthe temporal behavior and the recurrent activities of users which is essential\nto recommend the right item to the right user at the right time. In this paper,\nwe introduce Recurrent Poisson Factorization (RPF) framework that generalizes\nthe classical PF methods by utilizing a Poisson process for modeling the\nimplicit feedback. RPF treats time as a natural constituent of the model and\nbrings to the table a rich family of time-sensitive factorization models. To\nelaborate, we instantiate several variants of RPF who are capable of handling\ndynamic user preferences and item specification (DRPF), modeling the\nsocial-aspect of product adoption (SRPF), and capturing the consumption\nheterogeneity among users and items (HRPF). We also develop a variational\nalgorithm for approximate posterior inference that scales up to massive data\nsets. Furthermore, we demonstrate RPF's superior performance over many\nstate-of-the-art methods on synthetic dataset, and large scale real-world\ndatasets on music streaming logs, and user-item interactions in M-Commerce\nplatforms.","url_abs":"http://arxiv.org/abs/1703.01442v1","url_pdf":"http://arxiv.org/pdf/1703.01442v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"recurrent-poisson-factorization-for-temporal","repo_url":"https://github.com/AHosseini/RPF","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}