Skip to main content
Article
HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems
The 9th ACM Conference on Recommender Systems (2015)
  • Pigi Kouki, University of California Santa Cruz
  • Shobeir Fakhraei, University of Maryland
  • James Foulds, University of California Santa Cruz
  • Magdalini Eirinaki, San Jose State University
  • Lise Getoor, University of California Santa Cruz
Abstract
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.
Keywords
  • Hybrid recommender systems,
  • graphical models,
  • probabilistic programming,
  • probabilistic soft logic
Publication Date
October, 2015
Publisher Statement
SJSU users: use the following link to login and access the article via SJSU databases.
Citation Information
Pigi Kouki, Shobeir Fakhraei, James Foulds, Magdalini Eirinaki, et al.. "HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems" The 9th ACM Conference on Recommender Systems (2015)
Available at: http://works.bepress.com/magdalini_eirinaki/38/