Disentangled Graph Collaborative Filtering

By Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-seng Chua. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020

735 citations recorded by the source corpus.

Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance to the holistic interaction graph. Nevertheless, they largely model the relationships in a uniform manner, while neglecting the diversity of user intents on adopting the items, which could be to pass time, for interest, or shopping for others like families. Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations. In this work, we pay special attention to user-item relationships at the finer granularity of user intents. We hence devise a new model, Disentangled Graph Collaborative Filtering (DGCF), to disentangle these factors and yield disentangled representations. Specifically, by modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. Meanwhile, we encourage independence of different intents. This leads to disentangled representations, effectively distilling information pertinent to each intent. We conduct extensive experiments on three benchmark datasets, and DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE. Further analyses offer insights into the advantages of DGCF on the disentanglement of user intents and interpretability of representations. Our codes are available in https://github.com/ xiangwang1223/disentangled_graph_collaborative_filtering.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9d4901609a00d42cba00ffcb3c337bd3830140f6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Liqiang Nie
5. Meng Wang
6. Yong Li
7. Chen Gao
8. Chao Huang

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Liqiang Nie
5. Meng Wang
6. Yong Li
7. Chen Gao
8. Chao Huang

Citations in the corpus

• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “Wang et al. [183] developed disentangled GNN to capture independent user intentions, which extends the set of candidate items in matching and guarantees the accuracy simultaneously.”
• LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation (2023, background): “Hypergraph-based Collaborative Filtering: • HyRec (Wang et al., 2020a) makes use of hypergraph to encode multi-order information between users and items.”
• Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback (2020, methodology): “Moreover, following the widely-used evaluation metrics [20, 34, 35, 42], we adopted precision@K, recall@K, and Normalized Discounted Cumulative Gain (NDCG@K) to evaluate the performance of methods.”
• Advances and Challenges in Conversational Recommender Systems: A Survey (2021, methodology): “To overcome this problem, the model should consider more sophisticated strategies such as recognizing reliable negative samples [22, 39, 185, 96, 22] as well as disentangling user preferences on items and attributes [109, 183].”
• Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (2022, methodology): “In general, GNN-based collaborative filtering methods [9, 31, 32] produce informative representations for users and items based on the aggregation scheme, which can be formulated to two stages:”
• Graph Neural Networks in Recommender Systems: A Survey (2020, background): “NDCG NGCF [155], LR-GCCF [18], LightGCN [55], AGCN [173], HashGNN [135], DGCN-BinCF [142], DHCF [66], DGCF [156], NIA-GCN [132], Multi-GCCF [133]”
• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, neutral): “DGCF [73] SIGIR 2020 Gowalla, Yelp, Amazon”
• UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation (2021, methodology): “In general, our investigations for 𝜆 and 𝛾 show that these two parameters are important to UltraGCN, which can flexibly adjust the learning weights for different relationships and should be carefully set.”
• Learning Intents behind Interactions with Knowledge Graph for Recommendation (2021, methodology): “This module can be simply achieved by applying a statistical measure, such as mutual information [2], Pearson correlation [33], and distance correlation [32, 33, 43], as a regularizer.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, methodology): “Thus, a natural extension is to mine the [20], PMLAM [21], pQCF [22], FAWMF [23] User Interacted items+UID (Free Embed+Heuristic Agg) Item IID (Free Embed) SVD++ [24] User Interacted items (Free Embed+Heuristic Agg) Item IID (Free Embed) NAIS [25] User Interacted items+UID (Free Embed+Heuristic Agg)”
• Hypergraph Contrastive Collaborative Filtering (2022, methodology): “The effect of 𝜏 is further studied in the following subsection.”