Game Recommendation Based on Dynamic Graph Convolutional Network.

Recent years have witnessed the popularity of game recommendation. Different from the other recommendation scenarios, the user and item properties in game recommendation usually exhibit highly dynamic properties, and may influence each other in the user-item interaction process. For taming such characters, so as to design a high quality recommender system tailored for game recommendation, in this paper, we design a dynamic graph convolutional network to highlight the user/item evolutionary features. More specifically, the graph neighbors in our model are not static, they will be adaptively changed at different time. The recently interacted users or items are gradually involved into the aggregation process, which ensures that the user/item embeddings can evolve as the time goes on. In addition, to timely match the changed neighbors, we also update the convolutional weights in a RNN-manner. By these customized strategies, our model is expected to learn more accurate user behavior patterns in the field of game recommendation. We conduct extensive experiments on real-world datasets to demonstrate the superiority of our model.