Comparing the staples in latent factor models for recommender systems
Cheng Chen, Lantian Zheng, Alex Thomo, Kui Wu, Venkatesh Bharadwaj Srinivasan · 2014
Since the Netflix Prize competition, latent factor models (LFMs) have become the comparison "staples" for many of the recent recommender methods. The performance improvement of LFMs over baseline approaches, however, hovers at only low percentage numbers. Therefore, it is time for a better understanding of their real power beyond the overall RMSE (root-mean-square error), which as it happens, lies at a very compressed range, without providing too much chance for deeper insight. This paper provides a detailed experimental study regarding the performance of classical staple LFMs on a classical dataset, Movielens 1M1, that sheds light on a much more pronounced excellence of LFMs for particular categories of users and items, for RMSE and other measures. In particular, LFMs exhibit surprising and excellent advantages when handling several difficult user and item categories. By comparing the distributions of the test and predicted ratings, we show that the performance of LFMs is influenced by the rating distribution. We then propose a method to estimate the performance of LFMs for a given rating dataset. Also, we provide a very simple, open-source, library that implements staple LFMs achieving a similar performance as some very recent (2013) developments in LFMs, and at the same time being more transparent than some other libraries in wide use.