DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks

As the last stage of a typical recommendation system, collective recommendation aims to give the final touches to the recommended items and their layout so as to optimize overall objectives such as diversity and whole-page relevance. In practice, however, the interaction dynamics among the recommended items, their visual appearances and meta-data such as specifications are often too complex to be captured by experts' heuristics or simple models. To address this issue, we propose a <u>div</u> ersity-aware self-correcting sequential recommendation <u>net</u> works (DivNet) that is able to estimate utility by capturing the complex interactions among sequential items and diversify recommendations simultaneously. Experiments on both offline and online settings demonstrate that DivNet can achieve better results compared to baselines with or without collective recommendations.