Convolutional Neural Collaborative Filtering with Stacked Embeddings

Recommender System plays an important role in keeping people engaged with online services, and collaborative filtering is a main technique for recommendation. With the immense influence of deep learning, there is a growing interest in applying it to collaborative filtering. Existing methods have applied different ways to learn the user-item interaction function, however, most of these methods have limitation in modeling user-item correlations because they ignore the original user-item information and the large size of embeddings. In this work we propose Stacked Embedding Convolutional Neural Collaborative Filtering (SECNCF), a novel neural collaborative filtering architecture. The idea is to create a pedrail by stacking embeddings which are composed of user embedding, item embedding and latent factors. We apply convolutional neural network (CNN) above the pedrail layer to capture the local features of dimension correlations. This method is good at extracting rich local dimension correlations of embeddings and is scalable for modeling user-item interactions. Extensive experiments on three public accessible datasets show that our method makes significant improvement over the state-of-the-art methods.