Sequential Recommendation on Dynamic Heterogeneous Information Network

The sequential recommendation has been widely used to predict users' preferences in the near future by utilizing their dynamic interactions with items. However, existing methods only consider single-typed interactions (e.g., purchase), ignoring the rich heterogeneous information such as multi-typed interactions (e.g., click, purchase) and item attributes (e.g, category), which leads to a suboptimal model. We can integrate this rich information by introducing Dynamic Heterogeneous Information Networks (DHINs). Our solution contains three special designs: 1) Static Initialization; 2) Heterogeneous User Memory Network; 3) Two-level attention mechanism. Extensive experiments conducted on two real-world datasets show that our model outperforms other state-of-the-art solutions. Furthermore, we provide some insights into parameter settings and model interpretability.