{"type":"ScholarlyArticle","title":"SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation","authors":[{"name":"Chenwei Cai","slug":"chenwei-cai","given":"Chenwei","family":"Cai","affiliation":"University of California , San Diego"},{"name":"Ruining He","slug":"ruining-he","given":"Ruining","family":"He","affiliation":"University of California , San Diego"},{"name":"Julian McAuley","slug":"julian-mcauley","given":"Julian","family":"McAuley","affiliation":"University of California , San Diego"}],"abstract":"Dealing with sparse, long-tailed datasets, and cold-start problems is always a challenge for recommender systems. These issues can partly be dealt with by making predictions not in isolation, but by leveraging information from related events; such information could include signals from social relationships or from the sequence of recent activities. Both types of additional information can be used to improve the performance of state-of-the-art matrix factorization-based techniques. In this paper, we propose new methods to combine both social and sequential information simultaneously, in order to further improve recommendation performance. We show these techniques to be particularly effective when dealing with sparsity and cold-start issues in several large, real-world datasets.","doi":"10.24963/ijcai.2017/204","url":"https://ijcai.org/proceedings/2017/204","pdf":"https://ijcai.org/proceedings/2017/0204.pdf","markdown":"https://ijcai.org/proceedings/2017/204.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/204","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":204,"pages":{"first":1476,"last":1482},"keywords":["Machine Learning: Data Mining","Machine Learning: Machine Learning","Combinatorial & Heuristic Search: Modeling","Multidisciplinary Topics and Applications: AI and Social Sciences"],"videos":[],"references":[{"position":1,"raw":"[Bell et al., 2007] Robert M. 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