HyPER swMATH ID: 23917 Software Authors: Kouki, P.; Fakhraei, S.; Foulds, J.; Eirinaki, M.; Getoor, L. Description: HyPER: A flexible and extensible probabilistic framework for hybrid recommender systems. As the amount of recorded digital information increases, there is a growing need for flexible recommender systems which can incorporate richly structured data sources to improve recommendations. In this paper, we show how a recently introduced statistical relational learning framework can be used to develop a generic and extensible hybrid recommender system. Our hybrid approach, HyPER (HYbrid Probabilistic Extensible Recommender), incorporates and reasons over a wide range of information sources. Such sources include multiple user-user and item-item similarity measures, content, and social information. HyPER automatically learns to balance these different information signals when making predictions. We build our system using a powerful and intuitive probabilistic programming language called probabilistic soft logic, which enables efficient and accurate prediction by formulating our custom recommender systems with a scalable class of graphical models known as hinge-loss Markov random fields. We experimentally evaluate our approach on two popular recommendation datasets, showing that HyPER can effectively combine multiple information types for improved performance, and can outperform existing state-of-the-art approaches. Homepage: https://github.com/pkouki/recsys2015 Source Code: https://github.com/pkouki/recsys2015 Related Software: SNAP; node2vec; ProNE; DeepWalk; Tuffy; RockIt; Hyperband; ProbLog; Hyperopt; Eigentaste; Spearmint; ClaimEval; BLOG; Edward; mplp2; foxPSL; SPOOK; darch; FACTORIE; Church Cited in: 4 Documents all top 5 Cited by 13 Authors 3 Getoor, Lise 2 Bach, Stephen H. 2 Farnadi, Golnoosh 1 Augustine, Eriq 1 Broecheler, Matthias 1 De Cock, Martine 1 Dickens, Charles 1 Huang, Bert 1 Liao, Hao 1 Moens, Marie-Francine 1 Srinivasan, Sriram 1 Xu, Rong-Qin 1 Zhou, Mingyang Cited in 3 Serials 2 Machine Learning 1 Information Sciences 1 Journal of Machine Learning Research (JMLR) Cited in 5 Fields 3 Computer science (68-XX) 2 Game theory, economics, finance, and other social and behavioral sciences (91-XX) 1 Statistics (62-XX) 1 Operations research, mathematical programming (90-XX) 1 Biology and other natural sciences (92-XX) Citations by Year