Matrix factorization models have been widely used in recommender systems. We are interested in developing novel algorithms for matrix factorization and scaling to massive datasets. Recently we are also interested in applying deep learning in recommender systems, and it's interpretability.
SQL-Rank: A Listwise Approach to Collaborative Ranking (ICML '18)
We develop a scalable solver on pair-wise ranking loss, and show it outperforms (and is more stable) than traditional matrix factorization, with similar training time.
Large-scale Collaborative Ranking in Near-Linear Time (KDD '17)
We further apply the "list-wise loss" proposed in the learning-to-rank literature to personalized recommender system. We are able to make it as fast as point-wise and pair-wise models, and it performs much better in the implicit feedback setting.
As a 14-year online MOBA game player (starting with Warcraft III dota & Sanguo), I'm also interested in analyzing game data. In our recent paper, we are interested in the ranking problem based on match results, but different from classical ranking, each time we only observe a group comparison instead of pair comparison result.
Learning from Group Comparisons: Exploiting Higher Order Interactions (NIPS '18)
Matrix Completion with Noisy Side Information (NIPS '15)
Robust Principal Component Analysis with Side Information (ICML '16)
Thanks to my students (Liwei Wu, Yao Li) and collaborators (James Sharpnack, Kai-Yang Chiang, Inderjit Dhillon, Kevin Fujii, Fushing Hsieh) for all of these interesting work.