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Self-supervised learning for recommender systems: A survey
In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …
tremendous success, but they still fall short of expectation when dealing with highly sparse …
A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
XSimGCL: Towards extremely simple graph contrastive learning for recommendation
Contrastive learning (CL) has recently been demonstrated critical in improving
recommendation performance. The underlying principle of CL-based recommendation …
recommendation performance. The underlying principle of CL-based recommendation …
Are graph augmentations necessary? simple graph contrastive learning for recommendation
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of
recommendation, since its ability to extract self-supervised signals from the raw data is well …
recommendation, since its ability to extract self-supervised signals from the raw data is well …
Graph neural networks in recommender systems: a survey
With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …
alleviate such information overload. Due to the important application value of recommender …
Automated self-supervised learning for recommendation
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for
collaborative filtering (CF). To improve the representation quality over limited labeled data …
collaborative filtering (CF). To improve the representation quality over limited labeled data …
CrossCBR: cross-view contrastive learning for bundle recommendation
Bundle recommendation aims to recommend a bundle of related items to users, which can
satisfy the users' various needs with one-stop convenience. Recent methods usually take …
satisfy the users' various needs with one-stop convenience. Recent methods usually take …
Contrastive graph structure learning via information bottleneck for recommendation
Graph convolution networks (GCNs) for recommendations have emerged as an important
research topic due to their ability to exploit higher-order neighbors. Despite their success …
research topic due to their ability to exploit higher-order neighbors. Despite their success …
Contrastive self-supervised learning in recommender systems: A survey
Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …
years. However, these methods usually heavily rely on labeled data (ie, user-item …
Knowledge-adaptive contrastive learning for recommendation
By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based
recommender systems have shown their superiority in alleviating data sparsity and cold start …
recommender systems have shown their superiority in alleviating data sparsity and cold start …