Two-stage sequential recommendation for side information fusion and long-term and short-term preferences modeling

Sequential recommender systems aim to model users’ changing interests based on their historical behavior and predict what they will be interested in at the next moment. In recent years, approaches to modeling users’ long-term/short-term preferences have achieved promising results. Previous works typically model historical interactions through an end-to-end neural network incorporating rich side information, which relies on a final loss function to optimize all parameters. However, they tend to concatenate side information and item ID into a vector representation, leading to irreversible fusion. We propose a two-stage sequence recommendation framework to address this problem. The first stage aims to enhance the representation ability of sequence through a non-invasive bidirectional self-attentive item embedding. In the second stage, we use a time-interval aware Gated Recurrent Units with attention to capture the user’s latest intents, while predicting long-term preferences based on the first stage. To integrate the long-term/short-term preferences, we generate the final preference representation using an attention-based adaptive fusion module. We conduct extensive experiments on four benchmark datasets and the results demonstrate the effectiveness of our proposed model.