In the spring of 2013, Baidu, Inc. hosted a competition for teams to develop new algorithms for movie recommendation systems. The purpose of the competition was to develop better models for rating prediction and suggest methods for incorporating social media data into the prediction models. The goal of our project was to use the information disseminated by the top competitors of the contest to develop new algorithms for recommendation systems. Noting the prevalence of ensemble methods employed on factorization models, our team developed a doubly ensemble framework named TBEEF, a software framework with a plugin interface through which factorization and ensemble models could be easily developed and ran.
- Changes to previous version:
Included the final technical report.
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