Self-supervised Graph Learning for Recommendation

Wu, Jiancan; Wang, Xiang; Feng, Fuli; He, Xiangnan; Chen, Liang; Lian, Jianxun; Xie, Xing

doi:10.1145/3404835.3462862

Cited by 637 publications

(485 citation statements)

References 25 publications

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Section: Self-supervised Learningmentioning

confidence: 99%

“…As the research of self-supervised learning is still in its infancy, there are only several studies incorporating it with recommender systems [31], [41], [42], [43], [44], [45]. Some of these efforts mine the self-supervision signals from sequential data [31], [43], [45] and others capture the structure properties from user-item bipartite graph [41], [44] or social graph [42]. Different from the above approaches, our work is the first to consider the correlations between the collaborative features originated from interaction behaviors and user's/item's inherent content features, and we adopt the generative and contrastive self-supervised learning jointly under these two types of features to tackle the cold-start problem in recommendation.…”

Section: Self-supervised Learningmentioning

confidence: 99%

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Intent Disentanglement and Feature Self-supervision for Novel Recommendation

Qian¹,

Liang²,

Li³

et al. 2021

Preprint

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One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation studies over-emphasize the importance of tail items without differentiating the degree of users' intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing methods has ever taken the extreme case of tail items, i.e., cold-start items without any interaction, into consideration.In this work, we first disclose the mechanism that drives a user's interaction towards popular or niche items by disentangling her intent into conformity influence (popularity) and personal interests (preference). We then present a unified end-to-end framework to simultaneously optimize accuracy and novelty targets based on the disentangled intent of popularity and that of preference. We further develop a new paradigm for novel recommendation of cold-start items which exploits the self-supervised learning technique to model the correlation between collaborative features and content features. We conduct extensive experimental results on three real-world datasets. The results demonstrate that our proposed model yields significant improvements over the state-of-the-art baselines in terms of accuracy, novelty, coverage, and trade-off.

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Section: Self-supervised Learningmentioning

confidence: 99%

Section: Self-supervised Learningmentioning

confidence: 99%

Intent Disentanglement and Feature Self-supervision for Novel Recommendation

Qian¹,

Liang²,

Li³

et al. 2021

Preprint

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“…Despite the remarkable success, existing neural graph collaborative filtering methods still suffer from two major issues. Firstly, user-item interaction data is usually sparse or noisy, and it may not be able to learn reliable representations since the graph-based methods are potentially more vulnerable to data sparsity [33]. Secondly, existing GNN based CF approaches rely on explicit interaction links for learning node representations, while high-order relations or constraints (e.g., user or item similarity) cannot be explicitly utilized for enriching the graph information, which has been shown essentially useful in recommendation tasks [24,27,35].…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, existing GNN based CF approaches rely on explicit interaction links for learning node representations, while high-order relations or constraints (e.g., user or item similarity) cannot be explicitly utilized for enriching the graph information, which has been shown essentially useful in recommendation tasks [24,27,35]. Although several recent studies leverage constative learning to alleviate the sparsity of interaction data [33,39], they construct the contrastive pairs by randomly sampling nodes or corrupting subgraphs. It lacks consideration on how to construct more meaningful contrastive learning tasks tailored for the recommendation task [24,27,35].…”

Section: Introductionmentioning

confidence: 99%

“…a user (or an item), which is more efficient than the graph-level relations. We characterize these additional relations as enriched neighborhood of nodes, which can be defined in two aspects: (1) structural neighbors refer to structurally connected nodes by high-order paths, and (2) semantic neighbors refer to ing approaches (e.g., SGL [33]) that neglect the correlation among users (or items) and the proposed neighborhoodenriched contrastive learning approach (our approach).…”

Section: Introductionmentioning

confidence: 99%

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Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

Lin,

Tian,

Hou

et al. 2022

Preprint

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Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users' preference over items by modeling the user-item interaction graphs. Despite the effectiveness, these methods suffer from data sparsity in real scenarios. In order to reduce the influence of data sparsity, contrastive learning is adopted in graph collaborative filtering for enhancing the performance. However, these methods typically construct the contrastive pairs by random sampling, which neglect the neighboring relations among users (or items) and fail to fully exploit the potential of contrastive learning for recommendation.To tackle the above issue, we propose a novel contrastive learning approach, named Neighborhood-enriched Contrastive Learning, named NCL, which explicitly incorporates the potential neighbors into contrastive pairs. Specifically, we introduce the neighbors of a user (or an item) from graph structure and semantic space respectively. For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs. In implementation, the representations of users (or items) and neighbors correspond to the outputs of different GNN layers. Furthermore, to excavate the potential neighbor relation in semantic space, we assume that users with similar representations are within the semantic neighborhood, and incorporate these semantic neighbors into the prototype-contrastive objective. The proposed NCL can be optimized with EM algorithm and generalized to apply to graph collaborative filtering methods. Extensive experiments on five public datasets demonstrate the effectiveness of the proposed NCL, notably with 26% and 17% performance gain over a competitive graph collaborative filtering base model on the Yelp and Amazon-book datasets, respectively. Our implementation code is available at: https://github.com/RUCAIBox/NCL. CCS CONCEPTS• Information systems → Recommender systems. † Equal contribution.

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Self‐supervised graph learning for occasional group recommendation

Hao¹,

Yin²,

Zhang³

et al. 2022

Int J of Intelligent Sys

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As an important branch in Recommender System, occasional group recommendation has received more and more attention. In this scenario, each occasional group (cold‐start group) has no or few historical interacted items. As each occasional group has extremely sparse interactions with items, traditional group recommendation methods can not learn high‐quality group representations. The recent proposed Graph Neural Networks (GNNs), which incorporate the high‐order neighbors of the target occasional group, can alleviate the above problem in some extent. However, these GNNs still can not explicitly strengthen the embedding quality of the high‐order neighbors with few interactions. Motivated by the self‐supervised learning technique, which is able to find the correlations within the data itself, we propose a self‐supervised graph learning framework, which takes the user/item/group embedding reconstruction as the pretext task to enhance the embeddings of the cold‐start users/items/groups. To explicitly enhance the high‐order cold‐start neighbors' embedding quality, we further introduce an embedding enhancer, which leverages the self‐attention mechanism to improve the embedding quality for them. Comprehensive experiments show the advantages of our proposed framework than the state‐of‐the‐art methods.

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Self-supervised Graph Learning for Recommendation

Cited by 637 publications

References 25 publications

Intent Disentanglement and Feature Self-supervision for Novel Recommendation

Intent Disentanglement and Feature Self-supervision for Novel Recommendation

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

Self‐supervised graph learning for occasional group recommendation

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