Graph Collaborative Filtering Research Articles

LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive Learning

Graph collaborative filtering (GCF) has achieved exciting recommendation performance with its ability to aggregate high-order graph structure information. Recently, contrastive learning (CL) has been incorporated into GCF to alleviate data sparsity and noise issues. However, most of the existing methods employ random or manual augmentation to produce contrastive views that may destroy the original topology and amplify the noisy effects. We argue that such augmentation is insufficient to produce the optimal contrastive view, leading to suboptimal recommendation results. In this article, we proposed a L earnable M odel A ugmentation C ontrastive L earning (LMACL) framework for recommendation, which effectively combines graph-level and node-level collaborative relations to enhance the expressiveness of collaborative filtering (CF) paradigm. Specifically, we first use the graph convolution network (GCN) as a backbone encoder to incorporate multi-hop neighbors into graph-level original node representations by leveraging the high-order connectivity in user-item interaction graphs. At the same time, we treat the multi-head graph attention network (GAT) as an augmentation view generator to adaptively generate high-quality node-level augmented views. Finally, joint learning endows the end-to-end training fashion. In this case, the mutual supervision and collaborative cooperation of GCN and GAT achieves learnable model augmentation. Extensive experiments on several benchmark datasets demonstrate that LMACL provides a significant improvement over the strongest baseline in terms of Recall and NDCG by 2.5%–3.8% and 1.6%–4.0%, respectively. Our model implementation code is available at https://github.com/LiuHsinx/LMACL .

Enhanced Sub-graph Reconstruction Graph Neural Network for Recommendation

ABSTRACT Personalized recommendation can recommend items of interest to different users and is widely used in the real world. Among them, graph collaborative filtering is a method of personalized recommendation. It can enrich the connection between users and items on the basis of collaborative filtering, to learn the embedded representation of nodes more accurately. Since graph collaborative filtering is based on bipartite graphs, few exciting graph collaborative methods consider the relationships between users (or items), the message between homogeneous nodes are diluted or ignored. Predicting and constructing the relationship between users (or items) has become a challenging. To solve this problem, we propose an enhanced sub-graph reconstruction graph neural network for recommendation (SRCF), using a heterogeneous graph neural network based encoder-decoder learn potential relationships between users (or items), and reconstruct sub-graphs based on those relationships. In the proposed model, the information of user and item sub-graphs is merged with the network of graph collaborative filtering, which enhances effective information transfer between homogeneous nodes, thereby improving the model performance. We have selected a number of data sets of different scenarios and different scales to comprehensively evaluate the performance of the model, and the experimental results confirmed the superiority of our model.

PCG: A joint framework of graph collaborative filtering for bug triaging

AbstractBug triaging is a vital process in software maintenance, involving assigning bug reports to developers in the issue tracking system. Current studies predominantly treat automatic bug triaging as a classification task, categorizing bug reports using developers as labels. However, this approach deviates from the essence of triaging, which is establishing bug–developer correlations. These correlations should be explicitly leveraged, offering a more comprehensive and promising paradigm. Our bug triaging model utilizes graph collaborative filtering (GCF), a method known for handling correlations. However, GCF encounters two challenges in bug triaging: data sparsity in bug fixing records and semantic deficiency in exploiting input data. To address them, we propose PCG, an innovative framework that integrates prototype augmentation and contrastive learning with GCF. With bug triaging modeled as predicting links on the bipartite graph of bug–developer correlations, we introduce prototype clustering‐based augmentation to mitigate data sparsity and devise a semantic contrastive learning task to overcome semantic deficiency. Extensive experiments against competitive baselines validate the superiority of PCG. This work may open new avenues for investigating correlations in bug triaging and related scenarios.

Joint contrastive learning of structural and semantic for graph collaborative filtering

Recently, graph collaborative filtering has been proposed as a superior recommendation technique, due to its outstanding capability of capturing high-order correlations from user–item interactions. Despite effectiveness, it still suffers from two limitations on representation learning: (1) imbalanced data distribution. User/item nodes with high-degree usually interfere with the representation learning process of low-degree nodes, deteriorating the recommendation accuracy; (2) structural noise vulnerability. The neighborhood aggregation scheme for the node representing could easily amplify structural noises, damaging the robustness of recommenders. The latest studies have made progress in overcoming the two limitations by capturing self-supervision signals via contrastive learning. Nevertheless, they somehow rely on heuristics to design, at a certain distance from a more comprehensive settlement.To make contrastive learning more entrenched with graph collaborative filtering, this paper proposes a multi-task framework JCG that integrates joint contrastive learning strategies with a backbone of graph-based recommender. Specifically, (1) we design six contrastive learning tasks categorized as structural and semantic, and obtain nine joint strategies by combining two tasks from each category; (2) we systematically study the effect of joint strategies on graph collaborative filtering, finding that JCG with feasible strategies could achieve up to 18.5% and 17.9% performance gain over the backbone on datasets of Yelp and ML, respectively; (3) performance comparison with other competitive baselines demonstrates the superiority of our JCG, especially that on mitigating the two limitations of data imbalance and noise vulnerability. We hope that our conclusion could provide new insight for future studies.

Research of online courses recommendation based on deep learning

This paper delves into leveraging deep learning techniques, such as graph neural networks (GNNs), Transformer, and techniques in Large Language Models (LLMs), to enhance course recommendation systems in e-learning platforms. Recommendation methods have some short-comes in the case of online course with less information and choic less logic. Our research proposes novel algorithms that use graph collaborative filtering and sequential recommendation to improve recommendation accuracy and personalization. By analyzing user behavior patterns and course attributes, our approach aims to provide smarter and more efficient course recommendation services, ultimately enhancing learning outcomes and experiences in e-learning environments. This research not only contributes to the advancement of e-learning technology but also provides valuable insights for the broader application of deep learning in smart education.

LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Graph Collaborative Filtering (GCF) methods have emerged as an effective recommendation approach, capturing users’ preferences over items by modeling user–item interaction graphs. However, these methods suffer from data sparsity in real scenarios, and their performance can be improved using contrastive learning. In this paper, we propose an optimized method, named LoRA-NCL, for GCF based on Neighborhood-enriched Contrastive Learning (NCL) and low-rank dimensionality reduction. We incorporate low-rank features obtained through matrix factorization into the NCL framework and employ LightGCN to extract high-dimensional representations. Extensive experiments on five public datasets demonstrate that the proposed method outperforms a competitive graph collaborative filtering base model, achieving 4.6% performance gains on the MovieLens dataset, respectively.

GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning.

Studies have shown that the mechanism of action of many drugs is related to miRNA. In-depth research on the relationship between miRNA and drugs can provide theoretical foundations and practical approaches for various areas, such as drug target discovery, drug repositioning and biomarker research. Traditional biological experiments to test miRNA-drug susceptibility are costly and time-consuming. Thus, sequence- or topology-based deep learning methods are recognized in this field for their efficiency and accuracy. However, these methods have limitations in dealing with sparse topologies and higher-order information of miRNA (drug) feature. In this work, we propose GCFMCL, a model for multi-view contrastive learning based on graph collaborative filtering. To the best of our knowledge, this is the first attempt that incorporates contrastive learning strategy into the graph collaborative filtering framework to predict the sensitivity relationships between miRNA and drug. The proposed multi-view contrastive learning method is divided into topological contrastive objective and feature contrastive objective: (1) For the homogeneous neighbors of the topological graph, we propose a novel topological contrastive learning method via constructing the contrastive target through the topological neighborhood information of nodes. (2) The proposed model obtains feature contrastive targets from high-order feature information according to the correlation of node features, and mines potential neighborhood relationships in the feature space. The proposed multi-view comparative learning effectively alleviates the impact of heterogeneous node noise and graph data sparsity in graph collaborative filtering, and significantly enhances the performance of the model. Our study employs a dataset derived from the NoncoRNA and ncDR databases, encompassing 2049 experimentally validated miRNA-drug sensitivity associations. Five-fold cross-validation shows that the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPR) and F1-score (F1) of GCFMCL reach 95.28%, 95.66% and 89.77%, which outperforms the state-of-the-art (SOTA) method by the margin of 2.73%, 3.42% and 4.96%, respectively. Our code and data can be accessed at https://github.com/kkkayle/GCFMCL.

DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering

In tag-aware recommender systems, users are strongly encouraged to utilize arbitrary tags to mark items of interest. These user-defined tags can be viewed as a bridge linking users and items. Most tag-aware recommendation models focus on improving the accuracy by introducing ingenious design or complicated structures to handle the tagging information appropriately. Beyond accuracy, diversity is considered to be another important indicator affecting the user satisfaction. Recommending more diverse items will provide more interesting items and commercial sales. Therefore, we propose a diversified tag-aware recommendation model based on graph collaborative filtering. The proposed model establishes a generic graph collaborative filtering framework tailored for tag-aware recommendations. To promote diversity, we adopt two modules: personalized category-boosted negative sampling to select a certain proportion of similar but negative items as negative samples for training, and adversarial learning to make the learned item representation category-free. To improve accuracy, we conduct a two-way TransTag regularization to model the relationship among users, items, and tags. Blending these modules into the proposed framework, we can optimize both the accuracy and diversity concurrently in an end-to-end manner. Experiments on Movielens datasets show that the proposed model can provide diverse recommendations while maintaining a high level of accuracy.

SimDCL: dropout-based simple graph contrastive learning for recommendation

Representation learning of users and items is the core of recommendation, and benefited from the development of graph neural network (GNN), graph collaborative filtering (GCF) for capturing higher order connectivity has been successful in the recommendation domain. Nevertheless, the matrix sparsity problem in collaborative filtering and the tendency of higher order embeddings to smooth in GNN limit further performance improvements. Contrastive learning (CL) was introduced into GCF and alleviated these problems to some extent. However, existing methods usually require graph perturbation to construct augmented views or design complex CL tasks, which limits the further development of CL-based methods in the recommendation. We propose a simple CL framework that does not require graph augmentation, but is based on dropout techniques to generate contrastive views to address the aforementioned problem. Specifically, we first added dropout operation to the GNN computation, and then fed the same batch of samples twice into the network for computation. Using the randomness of dropout, a pair of views with random noise was obtained, and maximizing the similarity of the view pairs is set as an auxiliary task to complement the recommendation. In addition, we made a simple modification to the computation of the GNN to alleviate the information loss due to embedding smoothing by means of cross-layer connected graph convolution computation. We named our proposed method as Simple Contrastive Learning Graph Neural Network based on dropout (SimDCL). Extensive experiments on five public datasets demonstrate the effectiveness of the proposed SimDCL, especially on the Amazon Books and Ta-Feng datasets, where our approach achieves 44% and 43% performance gains compared to baseline.

Enhancing recommendations with contrastive learning from collaborative knowledge graph

There have been excellent results using knowledge graphs in recommender systems. Knowledge graphs can be used as auxiliary information to alleviate data sparsity and strengthen the modeling of item sets and the representation of user preferences. However, users as the Core subject in the recommendation process, should be taken seriously. We believe that the user's choice of items will be affected by internal and external factors. Internal factors refer to the users’ fuzzy interest sets, which initially affect the users' choices. External factors refer to the influence of similar users and similar items in the users' selection of items. Inspired by the success of contrastive learning in graph collaborative filtering, we propose the Knowledge Augmented User Representation (KAUR) model to explore contrastive learning in collaborative knowledge graphs, learning semantic neighbors (external factors) and extract fuzzy interest sets (internal factors) from collaborative knowledge graphs. Specifically, we use the graph neural network to learn the representation of each node in the collaborative knowledge graph and regard the information of nodes and their propagated neighbors’ information as positive contrastive pairs, and then use contrastive learning to enhance the node representations. To further explore the potential interests of users, we regard users (or items) with other similar users (or items) as semantic neighbors and incorporate them into contrastive learning as positive pairings as well. Then the extracted fuzzy interest sets are merged into the user representations to get better interpretability. We conduct extensive experiments on three standard datasets and the results show that our KAUR model outperforms current state-of-the-art baselines.

Exploiting high-order behaviour patterns for cross-domain sequential recommendation

The cross-domain sequential recommendation aims to predict the next item based on a sequence of recorded user behaviours in multiple domains. We propose a novel Cross-domain Sequential Recommendation approach with Graph-Collaborative Filtering (CsrGCF) to alleviate the sparsity issue of user-interaction data. Specifically, we design time-aware and relation-aware graph attention mechanisms with collaborative filtering to exploit high-order behaviour patterns of users for promising results in both domains. Time-aware Graph Attention mechanism (TGAT) is designed to learn the inter-domain sequence-level representation of items. Relationship-aware Graph Attention mechanism (RGAT) is proposed to learn collaborative items' and users' feature representations. Moreover, to simultaneously improve the recommendation performance in the two domains, a Cross-domain Feature Bidirectional Transfer module (CFBT) is proposed, transferring user's common sharing features in both domains and retaining user's domain-specific features in a specific domain. Finally, cross-domain and sequential information jointly recommend the next items that users like. We conduct extensive experiments on two real-world datasets that show that CsrGCF outperforms several state-of-the-art baselines in terms of Recall and MRR. These demonstrate the necessity of exploiting high-order behaviour patterns of users for a cross-domain sequential recommendation. Meanwhile, retaining domain-specific features is an important step in the process of cross-domain feature bidirectional transferring.

Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering

Recommendation systems often use side information to both alleviate problems, such as the cold start problem and data sparsity, and increase prediction accuracy. One such piece of side information, which has been widely investigated in addressing such challenges, is trust. However, the difficulty in obtaining explicit relationship data has led researchers to infer trust values from other means such as the user-to-item relationship. This paper proposes a model to improve prediction accuracy by applying the trust relationship between the user and item ratings. Two approaches to implement trust into prediction are proposed: one involves the use of estimated trust, and the other involves the initial trust. The efficiency of the proposed method is verified by comparing the obtained results with four well-known methods, including the state-of-the-art deep learning-based method of neural graph collaborative filtering (NGCF). The experimental results demonstrate that the proposed method performs significantly better than the NGCF, and the three other matrix factorization methods, namely, the singular value decomposition (SVD), SVD++, and the social matrix factorization (SocialMF).