Two-stage Sequential Recommendation via Bidirectional Attentive Behavior Embedding and Long/Short-term Integration

In E-commerce applications, to predict what users will buy next is a crucial mission of sequential recommendation. Most frontier researches build end-to-end training models for sequential recommendation tasks via RNNs, CNNs or attentive models. However, a user's historical behavior sequence carries more complex contextual information than words. In this paper, we propose a two-stage user modeling framework for sequential recommendation, which is consisted by a Bidirectional Self-attentive Behavior Embedding and a Long/Short-term Sequential Behavior Predictor. Firstly, in order to expand perceivable information, a novel self-attentive behavior embedding method is proposed to learn semantic representations not only for items, but also for other important contextual factors (e.g. actions, categories and time). Then, with the pre-trained behavior embeddings, we propose a personalized memory network for Top-N recommendation. We use recurrent network to encode the short-term intent and learn the personalized long-term memory by a self-attention block. To integrate the long/short-term preferences, we generate the predicted behavior representation by using the present intent as a query to match with user's historical preferences via attentive memory reader. Finally, we conduct extensive experiments on two benchmark datasets provided by Tmall and Amazon. Compared with state-of-the-art techniques, experimental results demonstrate the effectiveness of our proposed framework.