EIGENREC: An Efficient and Scalable Latent Factor Family for Top-N Recommendation

Sparsity presents one of the major challenges of Collaborative Filtering. Graph-based methods are known to alleviate its effects, however their use is often computationally prohibitive; Latent-Factor methods, on the other hand, present a reasonable and viable alternative. In this paper, we introduce EigenRec; a versatile and efficient Latent-Factor framework for Top-N Recommendations, that generalizes the well-known PureSVD algorithm (a) providing intuition about its inner structure, (b) paving the path towards improving its efficacy and, at the same time, (c) reducing its complexity. One of our central goals in this work is to ensure the applicability of our method in realistic big-data scenarios. To this end, we propose building our model using a computationally efficient Lanczos-based procedure, we discuss its Parallel Implementation in distributed computing environments, and we verify its favourable performance using real-world datasets. Furthermore, from a qualitative point of view, a comprehensive set of experiments on the MovieLens and the Yahoo!R2Music datasets based on widely applied performance metrics, indicate that EigenRec outperforms several state-of-the-art algorithms, in terms of Standard and Long-Tail recommendation accuracy, exhibiting low susceptibility to sparsity, even in its most extreme manifestations -- the Cold-Start problems.