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Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Published: 12 October 2020 Publication History

Abstract

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.

Supplementary Material

MP4 File (3394171.3413556.mp4)
In this work, we focused on the graph convolutional networks based recommendation methods. Exploring the existing arts (e.g. GraphSAGE, NGCF, and MMGCN), we found that the implicit feedback challenges GCN-based models worse, since the false-positive feedback negatively affects the graph structure. In the video presentation, we provided an illustration to analyze the problem. To address this challenge, we developed a solution, termed Graph-Refined Convolutional Graph (GRCN), which refines the structure of the user-item graph via measuring the affinity of each user-item pair. We also gave the schematic illustration of our proposed model to share with audiences. Finally, we provided the dataset and part of the empirical results to demonstrate the effectiveness of GRCN. Besides, at the end of the video, we gave the link and QR code to release the source code and parameters of the proposed method.

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    cover image ACM Conferences
    MM '20: Proceedings of the 28th ACM International Conference on Multimedia
    October 2020
    4889 pages
    ISBN:9781450379885
    DOI:10.1145/3394171
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    Published: 12 October 2020

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    Author Tags

    1. graph neural network
    2. implicit feedback
    3. multimedia recommendation

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    Funding Sources

    • the Innovation Teams in Colleges and Universities in Jinan
    • the Shandong Provincial Natural Science Foundation
    • the National Natural Science Foundation of China

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    • (2025)TPGRec: Text-enhanced and popularity-smoothing graph collaborative filtering for long-tail item recommendationNeurocomputing10.1016/j.neucom.2025.129539626(129539)Online publication date: Apr-2025
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