Learning Static and Dynamic Features for Collaborative Filtering
Xueyao Yang, Hong Jiang · 2019
User preferences are influenced by the purchased products, and ratings of products are also related to theirs public praises. Dynamic latent representations can be learned from these sequence information. Researches show that learning such dynamic features is helpful to build model-based collaborative filtering. However, static features also play an irreplaceable role in recommendations by reason of inherent characteristics of users/items. Ratings of users on products directly represent user preferences and qualities of products. A neural network model for learning both static and dynamic features is proposed in this paper. Autoencoder is adopted as a static model focusing on explicit feedback i.e. ratings, and gated recurrent unit is adopted as a dynamic model focusing on implicit feedback i.e. sequences. Features learned from static and dynamic models are combined to make predictions. Experiments on two real-word datasets i.e. Baby of Amazon dataset and MovieLens 10M show improvement of our proposed model over the state-of-the-art methods.