Hierarchical User Intent Graph Network forMultimedia Recommendation

TL;DR

This paper introduces a hierarchical graph network that captures multi-level user intents from co-interacted items to improve multimedia recommendation accuracy, demonstrating significant performance gains and interpretability.

Contribution

The work proposes a novel hierarchical user intent graph network that models multi-level user intents through intra- and inter-level aggregation, enhancing recommendation quality.

Findings

Achieves significant improvements over state-of-the-art methods.

Effectively captures multi-level user intents.

Provides interpretable user intent semantics.

Abstract

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