S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

Zhou, Kun; Wang, Hui; Zhao, Wayne Xin; Zhu, Yutao; Wang, Sirui; Zhang, Fuzheng; Wang, Zhongyuan; Wen, Ji-Rong

doi:10.1145/3340531.3411954

Cited by 422 publications

(299 citation statements)

References 19 publications

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“…As the research of self-supervised learning is still in its infancy, there are only several works combining it with recommender systems [24,44,45,64]. These efforts either mine self-supervision signals from future/surrounding sequential data [24,45], or mask attributes of items/users to learn correlations of the raw data [64]. However, these thoughts cannot be easily adopted to social recommendation where temporal factors and attributes may not be available.…”

Section: Self-supervised Learningmentioning

confidence: 99%

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Yin

et al. 2021

Proceedings of the Web Conference 2021

227

104

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Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences. However, real-life interactions among users are very complicated and user relations can be high-order. Hypergraph provides a natural way to model complex high-order relations, while its potentials for improving social recommendation are under-explored. In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations. Technically, each channel in the network encodes a hypergraph that depicts a common high-order user relation pattern via hypergraph convolution. By aggregating the embeddings learned through multiple channels, we obtain comprehensive user representations to generate recommendation results. However, the aggregation operation might also obscure the inherent characteristics of different types of high-order connectivity information. To compensate for the aggregating loss, we innovatively integrate self-supervised learning into the training of the hypergraph convolutional network to regain the connectivity information with hierarchical mutual information maximization. The experimental results on multiple real-world datasets show that the proposed model outperforms the SOTA methods, and the ablation study verifies the effectiveness of the multi-channel setting and the selfsupervised task. The implementation of our model is available via https://github.com/Coder-Yu/RecQ.

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Section: Self-supervised Learningmentioning

confidence: 99%

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Yin

et al. 2021

Proceedings of the Web Conference 2021

227

104

View full text Add to dashboard Cite

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“…However, the transitivity assumption of the messages is not always valid due to the existence of negative edges. Inspired by recent self-learning work with MI maximization [57,58] and knowledge graph embedding (KGE) [59][60][61], we propose a relation representation learning framework via signed graph mutual information maximization, and then use learned vector representation as the input of neural networks to perform the task of trust prediction.…”

Section: Proposed Methodsmentioning

confidence: 99%

Relation Representation Learning via Signed Graph Mutual Information Maximization for Trust Prediction

et al. 2021

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Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacting entities accurately. However, most of the existing methods infer the trust relation between interacting entities usually rely on modeling the similarity between nodes on a graph and ignore semantic relation and the influence of negative links (e.g., distrust relation). In this paper, we proposed a relation representation learning via signed graph mutual information maximization (called SGMIM). In SGMIM, we incorporate a translation model and positive point-wise mutual information to enhance the relation representations and adopt Mutual Information Maximization to align the entity and relation semantic spaces. Moreover, we further develop a sign prediction model for making accurate trust predictions. We conduct link sign prediction in trust networks based on learned the relation representation. Extensive experimental results in four real-world datasets on trust prediction task show that SGMIM significantly outperforms state-of-the-art baseline methods.

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“…This work conducts experiments on five common data sets collected from real-world platforms, which come from different domains and have different sparsity levels. To ensure that each user/item has enough interaction, we follow the preprocessing procedure in Zhou et al (2020), which only keeps the "5-core" data sets. This means that users and items with fewer than five interaction records are deleted.…”

Section: Data Setsmentioning

confidence: 99%

“…Yelp data set is collected by Yelp [3], which is the largest review site in the USA. We follow the preprocessing procedure in Zhou et al (2020) and use the transaction records after January 1st, 2019. In addition, we treat the categories of businesses as attributes.…”

Section: Data Setsmentioning

confidence: 99%

Behavioral data assists decisions: exploring the mental representation of digital-self

Zhang

Cui

et al. 2021

IJCS

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Purpose The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell. Design/methodology/approach In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making. Findings The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest. Originality/value In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

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S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

Cited by 422 publications

References 19 publications

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Relation Representation Learning via Signed Graph Mutual Information Maximization for Trust Prediction

Behavioral data assists decisions: exploring the mental representation of digital-self

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