SSRGAN: A Generative Adversarial Network for Streaming Sequential Recommendation

Studying the sequential recommendation in streaming settings becomes meaningful because large volumes of user-item interactions are generated in a chronological order. Although a few streaming update strategies have been developed, they cannot be applied in sequential recommendation, because they can hardly capture the long-term user preference only by updating the model with random sampled new instances. Besides, some latent information is ignored because the existing streaming update strategies are designed for individual interactions, without considering the interaction subsequence. In this paper, we propose a Streaming Sequential Recommendation with Generative Adversarial Network (SSRGAN) to solve the streaming sequential recommendation problem. To maintain the long-term memory and keep sequential information, we use the reservoir-based streaming storage mechanism and exploit an active subsequence selection strategy to update model. Moreover, to improve the effectiveness and efficiency of online model training, we propose a novel negative sampling strategy based on GAN to generate the most informative negative samples and use Gumble-Softmax to overcome the gradient block problem. We conduct extensive experiments on two real-world datasets and the results shows the superiority of our approaches in streaming sequential recommendation.