Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments

Abstract

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and (largely ad-hoc) hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann's [1999] aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. Global probabilistic models coupled with standard Expectation Maximization (EM) learning algorithms tend to drastically overfit in sparse-data situations, as is typical in recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the ResearchIndex library of Computer Science publications show that appropriate mixture models incorporating secondary data produce significantly better quality recommenders than k-nearest neighbors (k-NN). Global probabilistic models also allow more general inferences than local methods like k-NN.

Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)

References in corpus (4)

• Empirical Analysis of Predictive Algorithms for Collaborative Filtering
• Probabilistic Latent Semantic Analysis
• Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach
• Dependency Networks for Collaborative Filtering and Data Visualization

Cited by in corpus (8)

• Multi-Domain Collaborative Filtering
• Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
• Preference-based Graphic Models for Collaborative Filtering
• Statistical Significance of the Netflix Challenge
• Personalizing Image Search Results on Flickr
• Recommender systems in industrial contexts
• An Ontology-based Context Aware System for Selective Dissemination of Information in a Digital Library
• Recommender Systems for the Conference Paper Assignment Problem