Next-Item Recommendation With Deep Adaptable Co-Embedding Neural Networks

The next-item recommendation has been in the central of interest in real-world applications such as e-commerce. However, it is challenging to infer what a user may purchase next due the complex interactions in the historical sessions and the changing semantics of an item over time. Most existing methods employ separate models to generate the general preference and the sequential patterns for the next-item recommendation without considering the interactions between the two factors or use a simple linear combination of the two factors. In this paper, we propose a deep adaptable co-embedding neural network (ACENet) to address these limitations. ACENet not only adaptably balances the combination of general preference and sequential patterns but also introduces dynamic attention for each factor in hybrid representations. Extensive experiments on two real-world datasets show the superiority of ACENet compared with other state-of-the-art methods.