Neural Graph Collaborative Filtering
Xiang Wang,Xiangnan He,Meng Wang,Fuli Feng,Tat-Seng Chua +4 more
- pp 165-174
TLDR
Wang et al. as discussed by the authors proposed Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.Abstract:
Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect. In this work, we propose to integrate the user-item interactions - more specifically the bipartite graph structure - into the embedding process. We develop a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it. This leads to the expressive modeling of high-order connectivity in user-item graph, effectively injecting the collaborative signal into the embedding process in an explicit manner. We conduct extensive experiments on three public benchmarks, demonstrating significant improvements over several state-of-the-art models like HOP-Rec [39] and Collaborative Memory Network [5]. Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF. Codes are available at https://github.com/xiangwang1223/neural_graph_collaborative_filtering.read more
Citations
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Posted Content
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
TL;DR: This work proposes a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering, and is much easier to implement and train, exhibiting substantial improvements over Neural Graph Collaborative Filtering (NGCF) under exactly the same experimental setting.
Proceedings ArticleDOI
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
TL;DR: LightGCN as mentioned in this paper learns user and item embedding by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding.
Proceedings ArticleDOI
KGAT: Knowledge Graph Attention Network for Recommendation
TL;DR: Wang et al. as mentioned in this paper proposed a knowledge graph attention network (KGAT) which explicitly models the high-order connectivities in KG in an end-to-end fashion.
Proceedings ArticleDOI
KGAT: Knowledge Graph Attention Network for Recommendation
TL;DR: This work proposes a new method named Knowledge Graph Attention Network (KGAT), which explicitly models the high-order connectivities in KG in an end-to-end fashion and significantly outperforms state-of-the-art methods like Neural FM and RippleNet.
References
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