Compound classification models for recommender systems

Recommender systems recommend products to customers based on ratings or past customer behavior. Without any information about attributes of the products or customers involved, the problem has been tackled most successfully by a nearest neighbor method called collaborative filtering in the context, while additional efforts invested in building classification models did not pay off and did not increase the quality. Therefore, classification methods have mainly been used in conjunction with product or customer attributes. Starting from a view on the plain recommendation task without attributes as a multi-class classification problem, we investigate two particularities, its autocorrelation structure as well as the absence of re-occurring items (repeat buying). We adapt the standard generic reductions 1-vs-rest and 1-vs-l of multi-class problems to a set of binary classification problems to these particularities and thereby provide a generic compound classifier for recommender systems. We evaluate a particular specialization thereof using linear support vector machines as member classifiers on MovieLens data and show that it outperforms state-of-the-art methods, i.e., item-based collaborative filtering.