Multimodal-Adaptive Hierarchical Network For Multimedia Sequential Recommendation

Recommender system has a pivotal role in electronic economy especially for the online shopping platforms. Studies over the past two decades have proved that exploiting the inherent properties of items contributes a lot to the accuracy of multimedia sequential recommendation. There is no doubt that multimedia information including images and texts of a product have an impact on user's purchase decision. However, modeling user's dynamic preferences for multimodal (visual and textual in this paper) information over time is still a challenging problem. To solve this problem, we propose a Multimodal-Adaptive Hierarchical Network (MAHN for short) for multimedia sequential recommendation, which includes a hierarchical recurrent neural network and an information modulation module between the hierarchical structure. Specifically, the hierarchical recurrent neural network achieves the re-selection of multimodal information from the first layer to the second layer, the information modulation module realizes the selection of each modal information at time step t based on the previous time steps. Finally, to improve the generalization ability of our model, we adopt the multi-task training style to jointly optimize BPR loss and reconstruction loss of multimodal information. Experiments are conducted on two real world public datasets, and the results demonstrate that our model outperforms the other methods. (c) 2021 Elsevier B.V. All rights reserved.