activity
20232026
most citedRepresentation Learning with Large Language Models for Recommendation

215 citations · 270 across the 14 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.LG2024

EasyST: A Simple Framework for Spatio-Temporal Prediction

Jiabin Tang, Wei Wei, Lianghao Xia +1

Spatio-temporal prediction is a crucial research area in data-driven urban computing, with implications for transportation, public safety, and environmental monitoring. However, sc…

cs.IR2024★ 1 cited

DiffMM: Multi-Modal Diffusion Model for Recommendation

Yangqin Jiang, Lianghao Xia, Wei Wei +3

The rise of online multi-modal sharing platforms like TikTok and YouTube has enabled personalized recommender systems to incorporate multiple modalities (such as visual, textual, a…

cs.IR2024★ 22 cited

A Comprehensive Survey on Self-Supervised Learning for Recommendation

Xubin Ren, Wei Wei, Lianghao Xia +1

Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep lear…

cs.IR2024

PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning

Wei Wei, Jiabin Tang, Yangqin Jiang +2

Multimedia online platforms (e.g., Amazon, TikTok) have greatly benefited from the incorporation of multimedia (e.g., visual, textual, and acoustic) content into their personal rec…

cs.CL2024

HiGPT: Heterogeneous Graph Language Model

Jiabin Tang, Yuhao Yang, Wei Wei +4

Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for…

cs.LG2024★ 7 cited

GraphEdit: Large Language Models for Graph Structure Learning

Zirui Guo, Lianghao Xia, Yanhua Yu +4

Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural…