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 [1], which enables efficient and accurate prediction by formulating our custom recommender systems with a scalable class of graphical models known as hinge-loss Markov ra...

• Type: Journal
• Year: 2015
• Where: RECSYS
• Authors: Pigi Kouki, Shobeir Fakhraei, James R. Foulds, Magdalini Eirinaki, Lise Getoor
• Keywords: Control Systems, RECSYS 2015