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When large language models meet personalization: Perspectives of challenges and opportunities
The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …
intelligence. With the unprecedented scale of training and model parameters, the capability …
A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Llmrec: Large language models with graph augmentation for recommendation
The problem of data sparsity has long been a challenge in recommendation systems, and
previous studies have attempted to address this issue by incorporating side information …
previous studies have attempted to address this issue by incorporating side information …
LightGCL: Simple yet effective graph contrastive learning for recommendation
Graph neural network (GNN) is a powerful learning approach for graph-based recommender
systems. Recently, GNNs integrated with contrastive learning have shown superior …
systems. Recently, GNNs integrated with contrastive learning have shown superior …
Representation learning with large language models for recommendation
Recommender systems have seen significant advancements with the influence of deep
learning and graph neural networks, particularly in capturing complex user-item …
learning and graph neural networks, particularly in capturing complex user-item …
Hypergraph contrastive collaborative filtering
Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …
users and items into latent representation space, with their correlative patterns from …
Heterogeneous graph contrastive learning for recommendation
Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured
data in recommender systems. However, real-life recommendation scenarios usually involve …
data in recommender systems. However, real-life recommendation scenarios usually involve …
Improving graph collaborative filtering with neighborhood-enriched contrastive learning
Recently, graph collaborative filtering methods have been proposed as an effective
recommendation approach, which can capture users' preference over items by modeling the …
recommendation approach, which can capture users' preference over items by modeling the …
HGNN+: General Hypergraph Neural Networks
Graph Neural Networks have attracted increasing attention in recent years. However,
existing GNN frameworks are deployed based upon simple graphs, which limits their …
existing GNN frameworks are deployed based upon simple graphs, which limits their …
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 …