Search Results for author:
Found 10 papers, 9 papers with code
A Comprehensive Survey on Self-Supervised Learning for Recommendation
Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences.
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.
Representation Learning with Large Language Models for Recommendation
RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework.
How Expressive are Graph Neural Networks in Recommendation?
Most existing works adopt the graph isomorphism test as the metric of expressiveness, but this graph-level task may not effectively assess a model's ability in recommendation, where the objective is to distinguish nodes of different closeness.
SSLRec: A Self-Supervised Learning Framework for Recommendation
Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field.
Graph Transformer for Recommendation
This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture.
Disentangled Contrastive Collaborative Filtering
Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF).
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders.
Deep Deconfounded Content-based Tag Recommendation for UGC with Causal Intervention
We first establish a causal graph to represent the relations among uploader, UGC, and tag, where the uploaders are identified as confounders that spuriously correlate UGC and tag selections.
Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation
Recommending appropriate tags to items can facilitate content organization, retrieval, consumption and other applications, where hybrid tag recommender systems have been utilized to integrate collaborative information and content information for better recommendations.
