Modeling Both Collaborative and Temporal Information for Sequential Recommendation

Sequential recommendation has drawn a lot of attention due to its good performance in recent years. The temporal order of user interactions cannot be ignored in sequential recommendation, and user preferences are constantly changing over time. The application of deep neural network in sequential recommendation has achieved many remarkable results, especially self-attention based methods. However, previous works mainly focused on item-item temporal information of the sequence while ignoring the latent collaborative relations in user-item interactions. Therefore, we propose a new method named Collaborative-Temporal modeling for Sequential Recommendation (CTSR) to learn both collaborative relations and temporal information. The proposed CTSR method introduces a graph convolutional network to learn the user-item collaborative relations while using self-attention to capture item-item temporal information. We apply focal loss to reduce the loss contribution of the easy samples and increase the contribution of the hard samples, to train the model more effectively. We extract a portion of item-item pairs that are most valuable, and then feed these pairs as augmented information into adjacency matrix of the graph neural network. More importantly, it is the first work to encode the relative positions of items into their embeddings in sequential recommendation. The experimental results show that CTSR surpasses previous works on three real-world datasets.