EACoupledCF: An Enhanced Attention-based Coupled Collaborative Filtering Approach for Recommendation

Abstract

Recommender system is the core to solve the problem of information overload. Meanwhile, non-IID (non-Independently Identically Distribution) recommender system shows its potential in improving recommendation quality and solving the problems such as sparsity and cold start. With the development of deep learning, recommendation has become a hot topic and a large number of studies have proved the effectiveness of deep learning in recommender system. In this work, we contribute a new multi-layer neural network framework, EACoupledCF (Enhanced Attention-based Coupled Collaborative Filtering), to perform collaborative filtering. The idea of EACoupledCF is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space, utilize the convolutional neural network and introduce spatial attention mechanism to learn high-order features between embedded dimensions. At the same time, it also proposes a novel model called DCCF (Deep Combination Collaborative Filtering) for implicit feedback learning in order to capture the interactive information better. In contrast to the existing neural recommendation models, the experimental results obtained on two real-word large datasets show the effectiveness of our proposed model.