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In this work, we aim to learn multi-level user intents from the co-interactedpatterns of items, so as to obtain high-quality representations of users anditems and further enhance the recommendation performance. Towards this end, wedevelop a novel framework, Hierarchical User Intent Graph Network, whichexhibits user intents in a hierarchical graph structure, from the fine-grainedto coarse-grained intents. In particular, we get the multi-level user intentsby recursively performing two operations: 1) intra-level aggregation, whichdistills the signal pertinent to user intents from co-interacted item graphs;and 2) inter-level aggregation, which constitutes the supernode in higherlevels to model coarser-grained user intents via gathering the nodes'representations in the lower ones. Then, we refine the user and itemrepresentations as a distribution over the discovered intents, instead ofsimple pre-existing features. To demonstrate the effectiveness of our model, weconducted extensive experiments on three public datasets. Our model achievessignificant improvements over the state-of-the-art methods, including MMGCN andDisenGCN. Furthermore, by visualizing the item representations, we provide thesemantics of user intents.

Hierarchical User Intent Graph Network forMultimedia Recommendation