Restricted Boltzmann Machine Based Active Learning For Sparse Recommendation

In recommender systems, users' preferences are expressed as ratings (either explicit or implicit) for items. In general, more ratings associated with users or items are elicited, more effective the recommendations are. However, almost all user rating datasets are sparse in the real-world applications. To acquire more ratings, the active learning based methods have been used to selectively choose the items (called interview items) to ask users for rating, inspired by that the usefulness of each rating may vary significantly, i.e., different ratings may bring a different amount of information about the user's tastes. Nevertheless, existing active learning based methods, including both static methods and decision-tree based methods, encounter the following limitations. First, the interview item set is predefined in the static methods, and they do not consider the user's responses when asking the next question in the interview process. Second, the interview item set in the decision tree based methods is very small (i.e., usually less than 50 items), which leads to that the interview items cannot fully reflect or capture the diverse user interests, and most items do not have the opportunity to obtain additional ratings. Moreover, these decision tree based methods tend to choose popular items as the interview items instead of items with sparse ratings (i.e., sparse items), resulting in "Harry Potter Effect" (http://bickson.blogspot.com.au/2012/09/harry-potter-effect-on-recommendations.html). To address these limitations, we propose a new active learning framework based on RBM (Restricted Boltzmann Machines) to add ratings for sparse recommendation in this paper. The superiority of this method is demonstrated on two publicly available real-life datasets.