Content-based recommendation systems can provide recommendations

for “cold-start” items for which little or no training

data is available, but typically have lower accuracy than

collaborative filtering systems. Conversely, collaborative filtering

techniques often provide accurate recommendations,

but fail on cold start items. Hybrid schemes attempt to

combine these different kinds of information to yield better

recommendations across the board.

We describe unified Boltzmann machines, which are probabilistic

models that combine collaborative and content information

in a coherent manner. They encode collaborative

and content information as features, and then learn weights

that reflect how well each feature predicts user actions. In

doing so, information of different types is automatically weighted,

without the need for careful engineering of features or

for post-hoc hybridization of distinct recommender systems.

We present empirical results in the movie and shopping

domains showing that unified Boltzmann machines can be

used to combine content and collaborative information to

yield results that are competitive with collaborative technique

in recommending items that have been seen before,

and also effective at recommending cold-start items.

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In: ACM International Conference on Recommender Systems

Publisher: Association for Computing Machinery, Inc.
Copyright © 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.

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Asela Gunawardana and Christopher Meek. Tied Boltzmann Machines for Cold Start Recommendations, Association for Computing Machinery, Inc., October 2008.