This paper describes the solution of Shanda Innovations team to Task 1 of KDD-Cup 2012. A novel approach called Multifaceted Factorization Models is proposed to incorporate a great variety of features in social networks. Social relationships and actions between users are integrated as implicit feedbacks to improve the recommendation accuracy. Keywords, tags, profiles, time and some other features are also utilized for modeling user interests. In addition, user behaviors are modeled from the durations of recommendation records. A context-aware ensemble framework is then applied to combine multiple predictors and produce final recommendation results. The proposed approach obtained 0.43959 (public score) / 0.41874 (private score) on the testing dataset, which achieved the 2nd place in the KDD-Cup competition.

中文翻译：

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社交网络中用于推荐预测的多方面分解模型的上下文感知集成

本文介绍了盛大创新团队对KDD-Cup 2012任务1的解决方案。提出了一种名为“多方面分解模型”的新颖方法，以将社交网络中的多种功能纳入其中。用户之间的社交关系和行为被集成为隐式反馈，以提高推荐的准确性。关键字，标签，配置文件，时间和其他一些特征也用于对用户兴趣进行建模。此外，根据推荐记录的持续时间对用户行为进行建模。然后将上下文感知的集成框架应用于组合多个预测变量并产生最终推荐结果。所提出的方法在测试数据集上获得0.43959（公共分数）/0.41874（私有分数），在KDD-Cup竞赛中排名第二。