Search Results for author:
Found 11 papers, 5 papers with code
Multi-turn Response Selection with Commonsense-enhanced Language Models
SinLG takes advantage of Pre-trained Language Models (PLMs) to catch the word correlations in the context and response candidates and utilizes a Graph Neural Network (GNN) to reason helpful common sense from an external knowledge graph.
Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
For Limitation 2, we model items in a universal feature space by their description texts.
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation
Empirical results demonstrate that PTIA poses a significant threat to users' historical trajectories.
Imbalanced Node Classification Beyond Homophilic Assumption
minority classes to balance the label and topology distribution.
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e. g., dimension \& number of hidden layers).
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance.
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).
Decentralized Collaborative Learning Framework for Next POI Recommendation
On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner.
Socially-Aware Self-Supervised Tri-Training for Recommendation
Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.
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.
