Hybrid Recommendation Models for Binary User Preference Prediction Problem.

This paper presents detailed information of our solutions to the task 2 of KDD Cup 2011. The task 2 is called binary user preference prediction problem in the paper because it aims at separating tracks rated highly by specific users from tracks not rated by them, and the solutions of this task can be easily applied to binary user behavior data. In the contest, we firstly implemented many different models, including neighborhood-based models, latent factor models, content-based models, etc. Then, linear combination is used to combine different models together. Finally, we used robust post-processing to further refine the special user-item pairs. The final error rate is 2.4808% which placed number 2 in the Leaderboard.