Adaptive Anti-Bottleneck Multi-Modal Graph Learning Network for Personalized Micro-video Recommendation

Micro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. There exists a substantial amount of excellent efforts made to the micro-video recommendation task. Recently, homogeneous (or heterogeneous) GNN-based approaches utilize graph convolutional operators (or meta-path based similarity measures) to learn meaningful representations for users and micro-videos and show promising performance for the micro-video recommendation task. However, these methods may suffer from the following problems: (1) fail to aggregate information from distant or long-range nodes; (2) ignore the varying intensity of users' preferences for different items in micro-video recommendations; (3) neglect the similarities of multi-modal contents of micro-videos for recommendation tasks. In this paper, we propose a novel Adaptive Anti-Bottleneck Multi-Modal Graph Learning Network for personalized micro-video recommendation. Specifically, we design a collaborative representation learning module and a semantic representation learning module to fully exploit user-video interaction information and the similarities of micro-videos, respectively. Furthermore, we utilize an anti-bottleneck module to automatically learn the importance weights of short-range and long-range neighboring nodes to obtain more expressive representations of users and micro-videos. Finally, to consider the varying intensity of users' preferences for different micro-videos, we design and optimize an adaptive recommendation loss to train our model in an end-to-end manner. We evaluate our method on three real-world datasets and the results demonstrate that the proposed model outperforms the baselines.