Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments
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Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence, Seite 437--444. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (2001)

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmarm'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. However, global probabilistic models coupled with standard EM learning algorithms tend to drastically overfit in the sparsedata situations typical of recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the Researchlndex library of Computer Science publications show that appropriate mixture models incorporating secondary data produce significantly better quality recommenders than <i>k</i>-nearest neighbors (<i>k</i>-NN). Global probabilistic models also allow more general inferences than local methods like <i>k</i>-NN.
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