{"type":"ScholarlyArticle","title":"Understanding Users' Budgets for Recommendation with Hierarchical Poisson Factorization","authors":[{"name":"Yunhui Guo","slug":"yunhui-guo","given":"Yunhui","family":"Guo","affiliation":"Institute of Artificial Intelligence , College of Computer Science , Zhejiang University , China"},{"name":"Congfu Xu","slug":"congfu-xu","given":"Congfu","family":"Xu","affiliation":"Institute of Artificial Intelligence , College of Computer Science , Zhejiang University , China"},{"name":"Hanzhang Song","slug":"hanzhang-song","given":"Hanzhang","family":"Song","affiliation":"Institute of Artificial Intelligence , College of Computer Science , Zhejiang University , China"},{"name":"Xin Wang","slug":"xin-wang","given":"Xin","family":"Wang","affiliation":"Institute of Artificial Intelligence , College of Computer Science , Zhejiang University , China"}],"abstract":"People consume and rate products in online shopping websites. The historical purchases of customers reflect their personal consumption habits and indicate their future shopping behaviors. Traditional preference-based recommender systems try to provide recommendations by analyzing users' feedback such as ratings and clicks. But unfortunately, most of the existing recommendation algorithms ignore the budget of the users. So they cannot avoid recommending users with products that will exceed their budgets. And they also cannot understand how the users will assign their budgets to different products. In this paper, we develop a generative model named collaborative budget-aware Poisson factorization (CBPF) to connect users' ratings and budgets. The CBPF model is intuitive and highly interpretable. We compare the proposed model with several state-of-the-art budget-unaware recommendation methods on several real-world datasets. The results show the advantage of uncovering users' budgets for recommendation.","doi":"10.24963/ijcai.2017/247","url":"https://ijcai.org/proceedings/2017/247","pdf":"https://ijcai.org/proceedings/2017/0247.pdf","markdown":"https://ijcai.org/proceedings/2017/247.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/247","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":247,"pages":{"first":1781,"last":1787},"keywords":["Machine Learning: Data Mining","Uncertainty in AI: Approximate Probabilistic Inference","Machine Learning: Learning Preferences or Rankings","Machine Learning: Learning Graphical Models"],"videos":[],"references":[{"position":1,"raw":"[Blei et al., 2016] David M Blei, Alp Kucukelbir, and Jon D McAuliffe. 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