Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

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 lessinterested 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 GCNbased recommender model.

Mining Latent Structures for Multimedia Recommendation

“…• GRCN [38] is also one of the state-of-the-arts multimodal recommendation methods. It refines user-item interaction graph by identifying the false-positive feedback and prunes the corresponding noisy edges in the interaction graph.…”

“…MV-RNN [6] uses multimodal features for sequential recommendation in a recurrent framework. Recently, Graph Neural Networks (GNNs) have been introduced into recommendation systems [36,41,46] and especially multimodal recommendation systems [23,38,39]. MMGCN [39] constructs modal-specific graph and conduct graph convolutional operations, to capture the modal-specific user preference and distills the item representations simultaneously.…”

“…In this way, the learned user representation can reflect the users' specific interests on items. Following MMGCN, GRCN [38] focuses on adaptively refining the structure of interaction graph to discover and prune potential false-positive edges.…”

“…MMGCN [39] constructs modality-specific user-item interaction graphs to model user preferences specific to each modality. Following MMGCN, GRCN [38] utilizes multimodal features to refine user-item interaction graphs by identifying false-positive feedbacks and prunes the corresponding noisy edges.…”

Multi-trends Enhanced Dynamic Micro-video Recommendation

“…In recent years, the amount of searchable micro-videos has increased dramatically and exacerbated the need for recommender systems that can effectively mine users' preference and identify potentially interested micro-videos in a personalized manner. Due to the powerful representation learning capacity, the rapid development of deep learning techniques has nourished the research field of recommendation [17,24,33,41,42,57,58,62,65,67,68,70,73,74]. Such a development also gives rise to diverse models for video recommendation, which can be roughly categorized to collaborative filtering [2,29], content-based filtering [11,16,44,48,77], and hybrid ones [5,6,72].…”