🚀 Can LLMs improve representation learning in Recommender Systems? The answer is YES! Introducting 🎯 RLMRec, a model-agnostic framework that improves the performance of SOTA recommenders with LLMs! Paper: arxiv.org/abs/2310.15950 Code: github.com/HKUDS/RLMRec 🤗 Enjoy RLMRec!

🔥Let #LLM understand graphs directly? GraphGPT made it! 📢GraphGPT is a Graph Large Language Model, which aligns Large Language Models (LLMs) with Graphs. ✅ Project page: graphgpt.github.io ✅ Code: github.com/HKUDS/GraphGPT ✅ Paper: arxiv.org/abs/2310.13023

🎉 Excited to see RLMRec on the GitHub Daily Python Trending list! 🚀RLMRec leverages LLMs to enhance the performance of recommendation systems. 🔥 Welcome to read the RLMRec paper (arxiv.org/abs/2310.15950) and give it a star⭐️ on the GitHub :) github.com/HKUDS/RLMRec

Sharing our recent release: 🌟GraphGPT🌟- A Large Language Model for Graph Learning and Analysis. 🔥With GraphGPT, we aim to push the boundaries of LLMs to unlock deeper insights into the structural knowledge of graphs🔥 graphgpt.github.io github.com/HKUDS/GraphGPT

Very fortunate to work with Chao Huang, and don't hesitate to check out GraphGPT! 🤩🤩

🔥 Super excited to share our WSDM2024 Oral Paper 🚀 "SSLRec: A Self-Supervised Learning Framework for Recommendation" 📚 Paper Link: arxiv.org/abs/2308.05697 ⚙ Code Link: github.com/HKUDS/SSLRec 🤗 With SSLRec, you can: (1/n)

🚀 Excited to share our latest research work 🌟LLMRec🌟 #LLMRec (ACM WSDM'2024 Oral) arxiv.org/abs/2311.00423 github.com/HKUDS/LLMRec 🔥Discover how the combination of LLMs and Data Augmentation is enhancing recommender systems! #Recommendation🔥

🌟 #RLMRec 🚀 A new recommendation paradigm that harnesses the power of LLMs📚 1️⃣ LLM-empowered representation learning🎯 2️⃣ Model-agnostic learning framework💡 3️⃣ Alignment between semantic and user behavior✨ 👉Paper: arxiv.org/abs/2310.15950 👉Github: github.com/HKUDS/RLMRec

🤔Are you considering to utilize a Text-Attributed Graph (TAG) Dataset for recomender systems? 👉Now check our TAGs data released in the recent work (github.com/HKUDS/RLMRec)! 😀 It's glad to see the Graph-Text alignment can enhance the recommenders in the era of LLMs!

📢 Check out the released Text-Attributed Graph (TAG) Datasets in the our 🌟RLMRec🌟 repository. 🚀 Enhance your recommender systems with Large Language Models (LLMs) using these datasets. github.com/HKUDS/RLMRec

Our 🎯 RLMRec has now been accepted by the The Web Conference 2024! Thanks all! paper link: arxiv.org/abs/2310.15950 code link: github.com/HKUDS/RLMRec #TheWebConf #WWW2024

The Year of the Dragon

Thrilled to share some news! 🎉🎉 I'll be speaking at WSDM '24: The Seventeenth ACM International Conference on Web Search and Data Mining on Mar 04 - 08, 2024. I would love to see you there! - via #Whova Event Platform tinyurl.com/2djdugf6 #WSDM2024

Congratulations! HKU IDS, a team of excellence!

🔥Excited to share our multi-behavior RSs work CML (github.com/weiwei1206/CML) has hit 100 citations as a Best Paper Award nominee! We express our gratitude to Pro. Huang Chao Huang and extend congratulations to Lianghao. Here's to continuing a journey of impactful work!🌟.

🔥🔥WWW'2024 PromptMM: Simplifying sophisticated multi-modal encoder via knowledge distillation with prompt tuning. Thanks to Prof. Huang Chao Huang for the guidance🌹, and collaborators Yangqin👬🏻, Jiabin🤝, and Lianghao🌺. 🚀🚀Code: github.com/HKUDS/PromptMM

📣 New #KDD2024 paper! Our work 🌟HiGPT🌟: "Heterogeneous Graph Language Models" has been accepted by #KDD2024 🚀 First Large Language Model (LLM) for Heterogenous Graphs with Any Relationships🔥 📄 The paper: arxiv.org/abs/2402.16024 💻 The source code: github.com/HKUDS/HiGPT

💗Hello 🌟HKUDS🌟! 🚀 Excited to share our lab (✨Data Intelligence Lab@HKU✨) GitHub (github.com/HKUDS) has now gained over 🌟3k stars🌟 and close to 🔥400 Followers🔥 since last year! (Although it may not compare to CV and NLP domains) 😄 🥳 The credit for our current