Chao Chen

TL;DR: WEMAREC is presented, a weighted and ensemble matrix approximation method for accurate and scalable recommendation that builds upon the intuition that (sub)matrices containing more frequent samples of certain user/ item/rating tend to make more reliable rating predictions for these specific user/item/rating.

TL;DR: A new algorithm design framework is presented, which introduces new optimization objectives to guide stable matrix approximation algorithm design, and solves the optimization problem to obtain stable low-rank approximation solutions with good generalization performance.

TL;DR: A mixture-rank matrix approximation (MRMA) method is proposed, in which user-item ratings can be characterized by a mixture of LRMA models with different ranks, and a learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to MRMA.

TL;DR: Experimental studies on the MovieLens and Netflix datasets demonstrate that AdaError outperforms state-of-the-art adaptive learning rate methods in matrix approximation-based collaborative filtering and can achieve statistically significant improvements over state of theart collaborative filtering methods in both rating prediction accuracy and top-N recommendation accuracy.