Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

By Yin-wei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Tat-seng Chua. ACM Multimedia, 2020

395 citations recorded by the source corpus.

Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interaction graph, edges between user and item nodes function as the main element of GCNs to perform information propagation and generate informative representations. Nevertheless, an underlying challenge lies in the quality of interaction graph, since observed interactions with less-interested items occur in implicit feedback (say, a user views micro-videos accidentally). This means that the neighborhoods involved with such false-positive edges will be influenced negatively and the signal on user preference can be severely contaminated. However, existing GCN-based recommender models leave such challenge under-explored, resulting in suboptimal representations and performance. In this work, we focus on adaptively refining the structure of interaction graph to discover and prune potential false-positive edges. Towards this end, we devise a new GCN-based recommender model, Graph-Refined Convolutional Network (GRCN), which adjusts the structure of interaction graph adaptively based on status of model training, instead of remaining the fixed structure. In particular, a graph refining layer is designed to identify the noisy edges with the high confidence of being false-positive interactions, and consequently prune them in a soft manner. We then apply a graph convolutional layer on the refined graph to distill informative signals on user preference. Through extensive experiments on three datasets for micro-video recommendation, we validate the rationality and effectiveness of our GRCN. Further in-depth analysis presents how the refined graph benefits the GCN-based recommender model.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/e7e6082e19d6acdd4167ba93cbc01ac39fe1985d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chunyan Miao
2. Xin Zhou
3. Hongyu Zhou
4. Yuan You
5. Zhiqi Shen
6. Fei Zhao
7. Chengcui Zhang
8. Baocheng Geng

In Information Systems

1. Chunyan Miao
2. Xin Zhou
3. Zhiqi Shen

Citations in the corpus

• Deep Multimodal Data Fusion (2024, background): “…task [42, 182, 182, 208–210], cross-modal retrieval task [30, 204, 217], RGB-depth scene classification task [222], multimodal recommendation task [193, 194], neuroimaging-based disease classification task [158], 3D object localization with natural language descriptions [21], and object…”
• Bootstrap Latent Representations for Multi-modal Recommendation (2022, methodology): “We speculate there are two potential reasons leading to the suboptimal performance of MMGCN, GRCN, and DualGNN.”
• A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation (2022, methodology): “• GRCN [24] refines the user-item bipartite graph with the removal of false-positive edges for multimodal recommendation.”