Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments (2001) [69 citations — 7 self]
http://www.neci.nec.com/~lawrence/papers/recommend
http://www.cis.upenn.edu/~popescul/Publications/po
http://www.cis.upenn.edu/~popescul/Publications/po
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author = {Alexandrin Popescul and Rin Popescul and Lyle H. Ungar and David M. Pennock and Steve Lawrence},
title = {Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments},
booktitle = {In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence},
year = {2001},
pages = {437--444}
}
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Abstract:
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 Hofmann's 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 sparse-data situations typical of 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.
