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.

本文提出了一种称为多方面因子模型的新方法来改善社交网络中的推荐准确性，该方法将用户之间的社交关系、标签、档案等特征整合在一起，并采用隐性反馈，同时还利用用户行为模型来进行联合预测，并在 KDD-Cup 竞赛中获得了第二名的好成绩。