  Rating Prediction is a basic problem in Recommender System, and one of the
most widely used method is Factorization Machines(FM). However, traditional
matrix factorization methods fail to utilize the benefit of implicit feedback,
which has been proved to be important in Rating Prediction problem. In this
work, we consider a specific situation, movie rating prediction, where we
assume that watching history has a big influence on his/her rating behavior on
an item. We introduce two models, Latent Dirichlet Allocation(LDA) and
word2vec, both of which perform state-of-the-art results in training latent
features. Based on that, we propose two feature based models. One is the
Topic-based FM Model which provides the implicit feedback to the matrix
factorization. The other is the Vector-based FM Model which expresses the order
info of watching history. Empirical results on three datasets demonstrate that
our method performs better than the baseline model and confirm that
Vector-based FM Model usually works better as it contains the order info.
