Junling Hu

IJCAI Conference 2015 Conference Paper

A Space Alignment Method for Cold-Start TV Show Recommendations

• Shiyu Chang
• Jiayu Zhou
• Pirooz Chubak
• Junling Hu
• Thomas Huang

In recent years, recommendation algorithms have become one of the most active research areas driven by the enormous industrial demands. Most of the existing recommender systems focus on topics such as movie, music, e-commerce etc. , which essentially differ from the TV show recommendations due to the cold-start and temporal dynamics. Both effectiveness (effectively handling the cold-start TV shows) and efficiency (efficiently updating the model to reflect the temporal data changes) concerns have to be addressed to design real-world TV show recommendation algorithms. In this paper, we introduce a novel hybrid recommendation algorithm incorporating both collaborative user-item relationship as well as item content features. The cold-start TV shows can be correctly recommended to desired users via a so called space alignment technique. On the other hand, an online updating scheme is developed to utilize new user watching behaviors. We present experimental results on a real TV watch behavior data set to demonstrate the significant performance improvement over other state-of-the-art algorithms.

JMLR Journal 2003 Journal Article

Nash Q-Learning for General-Sum Stochastic Games

• Junling Hu
• Michael P. Wellman

We extend Q-learning to a noncooperative multiagent context, using the framework of general-sum stochastic games. A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games (defined by Q-values) that arise during learning. Experiments with a pair of two-player grid games suggest that such restrictions on the game structure are not necessarily required. Stage games encountered during learning in both grid environments violate the conditions. However, learning consistently converges in the first grid game, which has a unique equilibrium Q-function, but sometimes fails to converge in the second, which has three different equilibrium Q-functions. In a comparison of offline learning performance in both games, we find agents are more likely to reach a joint optimal path with Nash Q-learning than with a single-agent Q-learning method. When at least one agent adopts Nash Q-learning, the performance of both agents is better than using single-agent Q-learning. We have also implemented an online version of Nash Q-learning that balances exploration with exploitation, yielding improved performance. [abs] [ pdf ][ ps.gz ][ ps ]

ICML Conference 2000 Conference Paper

Experimental Results on Q-Learning for General-Sum Stochastic Games

• Junling Hu
• Michael P. Wellman

AAAI Conference 1998 Short Paper

Learning in Markov Games with Incomplete Information

• Junling Hu

ICML Conference 1998 Conference Paper

Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm

• Junling Hu
• Michael P. Wellman