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Found 10 papers, 5 papers with code
LLMRec: Large Language Models with Graph Augmentation for Recommendation
By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders.
Graph Agent: Explicit Reasoning Agent for Graphs
Graph embedding methods such as Graph Neural Networks (GNNs) and Graph Transformers have contributed to the development of graph reasoning algorithms for various tasks on knowledge graphs.
Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking
Initially, biomedical concepts are embedded using language models, and then embedding similarity is utilized to retrieve the top candidates.
Efficient On-Device Session-Based Recommendation
Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item.
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
Meanwhile, to compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher).
Fast-adapting and Privacy-preserving Federated Recommender System
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
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.
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task.
Overcoming Data Sparsity in Group Recommendation
Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members.
Generating Reliable Friends via Adversarial Training to Improve Social Recommendation
Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.
