💗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…
📣 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
🔥🔥WWW'2024 PromptMM: Simplifying sophisticated multi-modal encoder via knowledge distillation with prompt tuning. Thanks to Prof. Huang @huang_chao4969 for the guidance🌹, and collaborators Yangqin👬🏻, Jiabin🤝, and Lianghao🌺. 🚀🚀Code: github.com/HKUDS/PromptMM
🔥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 @huang_chao4969 and extend congratulations to Lianghao. Here's to continuing a journey of impactful work!🌟.
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
🤔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
📢 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
🌟 #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
🔥 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)
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.iogithub.com/HKUDS/GraphGPT
🎉 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
🚀 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!
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