  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.
