We just released a comprehensive survey paper on causal inference in recommender systems arxiv.org/pdf/2301.00910…. It covers widely-used strategies for bias mitigation, explanation, and generalization. Please check it out!

#KDD2023 is around the corner! My group will present 6 papers, 1 tutorial, and 1 workshop paper, showcasing our latest works on trustworthy AI, causal inference, GNN, and data-efficient learning. Look forward to catching up with old friends and meeting new friends!😎

Check our recent survey on knowledge editing for pretrained LLMs (:

Collaborative Large Language Model for Recommender Systems LinkedIn proposes a generative recommender system that tightly integrates the ID paradigm and LLM paradigm using extended vocabulary, novel pretraining and finetuning. 📝arxiv.org/abs/2311.01343 👨🏽‍💻github.com/yaochenzhu/llm…

Can we bridge the gap between natural language and recommendation? Check out CLLM4Rec, the first framework that tightly couples LLM with RS that enables faithful reasoning of user/item semantics with pretrained knowledge :) 📝arxiv.org/abs/2311.01343 📷github.com/yaochenzhu/llm…

🚀 Introducing PyGDebias v0.1.1, an open-source library dedicated to fairness in graph mining algorithms! PyGDebias comes packed with 26 graph datasets, making it a diverse and comprehensive toolkit for fairness research on graphs.

TEN strategies for Usable XAI for LLMs with case studies!! 📜 Codes: github.com/JacksonWuxs/Us… 📚 Paper: arxiv.org/abs/2403.08946 Welcome to read, star, and contribute :)