Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information.Self-supervised graph learning seeks to harness highorder collaborative filtering signals through unsupervised augmentation on the user-item bipartite graph, primarily leveraging a multi-task learning framework that includes both supervised recommendation loss and self-supervised contrastive loss.However, this separate design introduces additional graph convolution processes and creates inconsistencies in gradient directions due to disparate losses, resulting in prolonged training times and sub-optimal performance.In this study, we introduce a unified framework of Supervised Graph Contrastive Learning for recommendation (SGCL) to address these issues.SGCL uniquely combines the training of recommendation and unsupervised contrastive losses into a cohesive supervised contrastive learning loss, aligning both tasks within a single optimization direction for exceptionally fast training.Extensive experiments on three real-world datasets show that SGCL outperforms state-of-the-art methods, achieving superior accuracy and efficiency.

The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems (RecSys) have been persistent concerns, hindering their deployment in real-world applications. This paper presents a critical examination of the necessity of graph convolutions during the training phase and introduces an innovative alternative: the Light Post-Training Graph Ordinary-Differential-Equation (LightGODE). Our investigation reveals that the benefits of GCNs are more pronounced during testing rather than training. Motivated by this, LightGODE utilizes a novel post-training graph convolution method that bypasses the computation-intensive message passing of GCNs and employs a non-parametric continuous graph ordinary-differential-equation (ODE) to dynamically model node representations. This approach drastically reduces training time while achieving fine-grained post-training graph convolution to avoid the distortion of the original training embedding space, termed the embedding discrepancy issue. We validate our model across several real-world datasets of different scales, demonstrating that LightGODE not only outperforms GCN-based models in terms of efficiency and effectiveness but also significantly mitigates the embedding discrepancy commonly associated with deeper graph convolution layers. Our LightGODE challenges the prevailing paradigms in RecSys training and suggests re-evaluating the role of graph convolutions, potentially guiding future developments of efficient large-scale graph-based RecSys.

Recommender systems (RecSys) aim to predict users’ preferences based on historical interactions and content profiles, and they are vital components of many online services. However, the strict cold-start (SCS) issue, i.e., users/items have no prior interactions, poses significant challenges for RecSys. The existing methods seek to transfer content knowledge, collaborative filtering (CF) knowledge, or combine the two from the warm-start scenario towards the (strict) cold-start scenarios. However, these approaches either ignore the available information or model the information in rough manners such that the two types of knowledge interfere with each other, leading to ineffective and uncontrolled knowledge transfer. In this work, we propose a novel dual-teacher knowledge distillation (DTKD) framework that simultaneously and effectively transfers both content and CF knowledge. The proposed DTKD framework contains two teachers, one for each knowledge type, that is specifically designed according to the characteristics of the content and CF data to distill the knowledge fully. Soft scoring is calculated during the distillation to denoise and augment the original hard-labeled interactions. A knowledge fusion module is then proposed to collect the consensus of the two teachers’ opinions. Finally, DTKD transfers both content and CF knowledge into a student module that learns the shared viewpoints of the teachers. We conduct extensive experiments on real-world datasets under the warm-start as well as three different SCS settings (i.e., strict cold users, strict cold items, and strict cold users & items). Experimental results show that DTKD outperforms strong baselines by large margins under all settings, especially the SCS ones.

Graph Convolution Networks (GCNs) are widely considered state-of-the-art for collaborative filtering. Although several GCN-based methods have been proposed and achieved state-of-the-art performance in various tasks, they can be computationally expensive and time-consuming to train if too many layers are created. However, since the linear GCN model can be interpreted as a differential equation, it is possible to transfer it to an ODE problem. This inspired us to address the computational limitations of GCN-based models by designing a simple and efficient NODE-based model that can skip some GCN layers to reach the final state, thus avoiding the need to create many layers. In this work, we propose a Graph Neural Ordinary Differential Equation-based method for Collaborative Filtering (GODE-CF). This method estimates the final embedding by utilizing the information captured by one or two GCN layers. To validate our approach, we conducted experiments on multiple datasets. The results demonstrate that our model outperforms competitive baselines, including GCN-based models and other state-of-the-art CF methods. Notably, our proposed GODE-CF model has several advantages over traditional GCN-based models. It is simple, efficient, and has a fast training time, making it a practical choice for real-world situations.

Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactions, can be noisy for users/items with abundant interactions and insufficient for users/items with scarce interactions. Additionally, the adjacency matrix ignores user-user and item-item correlations, which can limit the scope of beneficial neighbors being aggregated.