Content-based recommendation systems can provide recommendationsfor “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.