ANAGL: A Noise-resistant and Anti-sparse Graph Learning for micro-video recommendation

Abstract

In recent years, Graph Convolutional Networks (GCNs) have seen widespread utilization within micro-video recommendation systems, facilitating the understanding of user preferences through interactions with micro-videos. Despite the commendable performance exhibited by GCN-based methodologies, several persistent issues demand further scrutiny. Primarily, most user-micro-video interactions involve implicit behaviors, such as clicks or abstentions, which may inadvertently capture irrelevant micro-video content, thereby introducing significant noise (false touches, low watch-ratio, low ratings) into users’ histories. Consequently, this noise undermines the efficacy of micro-video recommendations. Moreover, the abundance of micro-videos has resulted in fewer interactions between users and micro-video content. To tackle these challenges, we propose a noise-resistant and anti-sparse graph learning framework for micro-video recommendation. Initially, we construct a denoiser that leverages implicit multi-attribute information (e.g., watch-ratio, timestamp, ratings, etc.) to filter noisy data from user interaction histories. This process yields high-fidelity micro-video information, enabling a more precise modeling of users’ feature preferences. Subsequently, we employ a multi-view reconstruction approach and utilize cross-view self-supervised learning to gain insights into user and micro-video features. This strategic approach effectively mitigates the issue of data sparsity. Extensive experiments conducted on two publicly available micro-video recommendation datasets validate the effectiveness of our proposed method. For in-depth details and access to the code, please refer to our repository at “https://github.com/kbk12/ANAGL.git.”