SRecGAN: Pairwise Adversarial Training for Sequential Recommendation

Abstract

Sequential recommendation is essentially a learning-to-rank task under special conditions. Bayesian Personalized Ranking (BPR) has been proved its effectiveness for such a task by maximizing the margin between observed and unobserved interactions. However, there exist unobserved positive items that are very likely to be selected in the future. Treating those items as negative leads astray and poses a limitation to further exploiting its potential. To alleviate such problem, we present a novel approach, Sequential Recommendation GAN (SRecGAN), which learns to capture latent users’ interests and to predict the next item in a pairwise adversarial manner. It can be interpreted as playing a minimax game, where the generator would learn a similarity function and try to diminish the distance between the observed samples and its unobserved counterpart, whereas the discriminator would try to maximize their margin. This intense adversarial competition provides increasing learning difficulties and constantly pushes the boundaries of its performance. Extensive experiments on three real-world datasets demonstrate the superiority of our methods over some strong baselines and prove the effectiveness of adversarial training in sequential recommendation.

This work was supported by the National Key R&D Program of China [2020YFB1707903]; the National Natural Science Foundation of China [61872238, 61972254], the Huawei Cloud [TC20201127009], the CCF-Tencent Open Fund [RAGR20200105], and the Tencent Marketing Solution Rhino-Bird Focused Research Program [FR202001].