Session 10: Graph Learning

Date: Wednesday October 16, 15:15 PM – 16:20 PM (GMT+2)
Room: Petruzzelli Theater
Session Chair: Antonio Ferrara

• RES 🕓15A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation
  by Zixuan Yi (University of Glasgow) and Iadh Ounis (University of Glasgow)

  With the rapid development of online multimedia services, especially in e-commerce platforms, there is a pressing need for personalised recommender systems that can effectively encode the diverse multi-modal content associated with each item. However, we argue that existing multi-modal recommender systems typically use isolated processes for both feature extraction and modality encoding. Such isolated processes can harm the recommendation performance. Firstly, an isolated extraction process underestimates the importance of effective feature extraction in multi-modal recommendations, potentially incorporating non-relevant information, which is harmful to item representations. Second, an isolated modality encoding process produces disjoint embeddings for item modalities due to the individual processing of each modality, which leads to a suboptimal fusion of user/item representations for an effective user preferences prediction. We hypothesise that the use of a unified model for addressing both aforementioned isolated processes will enable the consistent extraction and cohesive fusion of joint multi-modal features, thereby enhancing the effectiveness of multi-modal recommender systems. In this paper, we propose a novel model, called Unified multi-modal Graph Transformer (UGT), which firstly leverages a multi-way transformer to extract aligned multi-modal features from raw data for top-k recommendation. Subsequently, we build a unified graph neural network in our UGT model to jointly fuse the multi-modal user/item representations derived from the output of the multi-way transformer. Using the graph transformer architecture of our UGT model, we show that the UGT model achieves significant effectiveness gains, especially when jointly optimised with the commonly used recommendation losses. Our extensive experiments conducted on three benchmark datasets demonstrate that our proposed UGT model consistently outperforms nine existing state-of-the-art recommendation approaches and by up to 13.97% over the best baseline.

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• RES 🕓15Information-Controllable Graph Contrastive Learning for Recommendation
  by Zirui Guo (Beijing University of Posts and Telecommunications), Yanhua Yu (Beijing University of Posts and Telecommunications), Yuling Wang (Hangzhou Dianzi University), Kangkang Lu (Beijing University of Posts and Telecommunications), Zixuan Yang (Beijing University of Posts and Telecommunications), Liang Pang (Chinese Academy of Sciences) and Tat-Seng Chua (National University of Singapore)

  In the evolving landscape of recommender systems, Graph Contrastive Learning (GCL) has become a prominent method for enhancing recommendation performance by alleviating the issue of data sparsity. However, existing GCL-based recommendations often overlook the control of shared information between the contrastive views. In this paper, we initially analyze and experimentally demonstrate these methods often lead to the issue of augmented representation collapse, where the representations between views become excessively similar, diminishing their distinctiveness. To address this issue, we propose the Information-Controllable Graph Contrastive Learning (IGCL) framework, a novel approach that focuses on optimizing the shared information between views to include as much relevant information for the recommendation task as possible while maintaining an appropriate level. In particular, we design the Collaborative Signals Enhanced Augmentation module to infuse the augmented representation with rich, task-relevant collaborative signals. Furthermore, the Information-Controllable Contrastive Learning module is designed to direct control over the magnitude of shared information between the contrastive views to avoid over-similarity. Extensive experiments on three public datasets demonstrate the effectiveness of IGCL, showcasing significant improvements in performance and the capability to alleviate augmented representation collapse.

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• RES 🕓15MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for Recommendations
  by Yuezihan Jiang (Kuaishou Technology), Changyu Li (Kuaishou Technology), Gaode Chen (Chinese Academy of Sciences), Peiyi Li (Kuaishou Technology), Qi Zhang (Kuaishou Technology), Jingjian Lin (Kuaishou Technology), Peng Jiang (Kuaishou Inc.), Fei Sun (China) and Wentao Zhang (Peking University)

  Multi-view Graph Learning is popular in recommendations due to its ability to capture relationships and connections across multiple views. Existing multi-view graph learning methods generally involve constructing graphs of views and performing information aggregation on view representations. Despite their effectiveness, they face two data limitations: Multi-focal Multi-source data noise and multi-source Data Sparsity. The former arises from the combination of noise from individual views and conflicting edges between views when information from all views is combined. The latter occurs because multi-view learning exacerbate the negative influence of data sparsity because these methods require more model parameters to learn more view information. Motivated by these issues, we propose MMGCL, a meta knowledge-enhanced multi-view graph contrastive learning framework for recommendations. To tackle the data noise issue, MMGCL extract meta knowledge to preserve important information from all views to form a meta view representation. It then rectifies every view in multi-learning frameworks, thus simultaneously removing the view-private noisy edges and conflicting edges across different views. To address the data sparsity issue, MMGCL performs meta knowledge transfer contrastive learning optimization on all views to reduce the searching space for model parameters and add more supervised signal. Besides, we have deployed MMGCL in a real industrial recommender system in China, and we further evaluate it on three benchmark datasets and a practical industry online application. Extensive experiments on these datasets demonstrate the state-of-the-art recommendation performance of MMGCL.

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• REPR 🕓10A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph
  by Daniele Malitesta (Université Paris-Saclay, CentraleSupélec, Inria), Claudio Pomo (Politecnico di Bari), Vito Walter Anelli (Politecnico di Bari), Alberto Carlo Maria Mancino (Politecnico di Bari), Tommaso Di Noia (Politecnico di Bari) and Eugenio Di Sciascio (Politecnico di Bari)

  Recently, graph neural networks (GNNs)-based recommender systems have encountered great success in recommendation. As the number of GNNs approaches rises, some works have started questioning the theoretical and empirical reasons behind their superior performance. Nevertheless, this investigation still disregards that GNNs treat the recommendation data as a topological graph structure. Building on this assumption, in this work, we provide a novel evaluation perspective on GNNs-based recommendation, which investigates the impact of the graph topology on the recommendation performance. To this end, we select some (topological) properties of the recommendation data and three GNNs-based recommender systems (i.e., LightGCN, DGCF, and SVD-GCN). Then, starting from three popular recommendation datasets (i.e., Yelp2018, Gowalla, and Amazon-Book) we sample them to obtain 1,800 size-reduced datasets that still resemble the original ones but can encompass a wider range of topological structures. We use this procedure to build a large pool of samples for which data characteristics and recommendation performance of the selected GNNs models are measured. Through an explanatory framework, we find strong correspondences between graph topology and GNNs performance, offering a novel evaluation perspective on these models.

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• IND 🕓10Country-diverted experiments for mitigation of network effects
  by Lina Lin (Google), Changping Meng (Google), Jennifer Brennan (Google), Jean Pouget-Abadie (Google), Ningren Han (Google), Shuchao Bi (Google) and Yajun Peng (Google)

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