Convex AUC Optimization for Top-N Recommendation with Implicit Feedback

In this paper, an effective collaborative filtering algorithm for top-N item recommendation with implicit feedback is proposed. The task of top-N item recommendation is to predict a ranking of items (movies, books, songs, or products in general) that can be of interest for a user based on earlier preferences of the user. We focus on implicit feedback where preferences are given in the form of binary events/ratings. Differently from state-of-the-art methods, the method proposed is designed to optimize the AUC directly within a margin maximization paradigm. Specifically, this turns out in a simple constrained quadratic optimization problem, one for each user. Experiments performed on several benchmarks show that our method significantly outperforms state-of-the-art matrix factorization methods in terms of AUC of the obtained predictions.