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A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …
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 …
Graph self-supervised learning: A survey
Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …
works have focused on (semi-) supervised learning, resulting in shortcomings including …
Self-supervised learning on graphs: Contrastive, generative, or predictive
Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …
while such success relies heavily on the massive and carefully labeled data. However …
Simple unsupervised graph representation learning
In this paper, we propose a simple unsupervised graph representation learning method to
conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss …
conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss …
A survey of graph neural networks in various learning paradigms: methods, applications, and challenges
In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …
many problems in computer vision, speech recognition, natural language processing, and …
Disencdr: Learning disentangled representations for cross-domain recommendation
Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-
Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich …
Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich …
A review-aware graph contrastive learning framework for recommendation
Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …
and item embedding learning, followed by the user-item interaction modeling. By utilizing …
Generative-contrastive graph learning for recommendation
By treating users' interactions as a user-item graph, graph learning models have been
widely deployed in Collaborative Filtering~(CF) based recommendation. Recently …
widely deployed in Collaborative Filtering~(CF) based recommendation. Recently …
Contrastive cross-domain sequential recommendation
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions
based on user's historical sequential interactions from multiple domains. Generally, a key …
based on user's historical sequential interactions from multiple domains. Generally, a key …