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

TL;DR

This paper introduces a unified probabilistic framework that combines collaborative and content-based recommendation methods, effectively addressing data sparsity issues and improving recommendation quality.

Contribution

It extends Hofmann's aspect model to integrate user, item, and content co-occurrence data, with the influence of each emerging naturally from the data.

Findings

Mixture models outperform k-NN in recommendation quality.

Secondary content data helps overcome data sparsity.

Global probabilistic models enable more general inferences.

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.