💗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
![weiwei__cs's tweet card. [WWW'2024] "PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning" - HKUDS/PromptMM](https://pbs.twimg.com/card_img/1986987141159657472/xrPnfk76?format=jpg&name=orig)
🔥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!🌟.
Congratulations! HKU IDS, a team of excellence!
【Congratulations to HKU IDS Scholar Prof Chao Huang and his collaborators on receiving the “2024 Frontiers of Science Award in Theoretical Computer and Information Sciences”!🥳】 Read the paper here: doi.org/10.1145/329250…
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
Our 🎯 RLMRec has now been accepted by the @TheWebConf 2024! Thanks all! paper link: arxiv.org/abs/2310.15950 code link: github.com/HKUDS/RLMRec #TheWebConf #WWW2024
![xubinrencs's tweet card. [WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation" - HKUDS/RLMRec](https://pbs.twimg.com/card_img/1987837313972600832/OWk1Dbxf?format=jpg&name=orig)
🚀 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!
Very fortunate to work with @huang_chao4969, and don't hesitate to check out GraphGPT! 🤩🤩
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
![huang_chao4969's tweet card. [SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models" - HKUDS/GraphGPT](https://pbs.twimg.com/card_img/1987199739184754688/kXaRvXp4?format=jpg&name=orig)
🤔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
🤔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!
🌟 #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
🚀 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🔥
🔥 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)
![xubinrencs's tweet card. [WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation" - HKUDS/SSLRec](https://pbs.twimg.com/card_img/1987199737959968768/RmDj58cK?format=jpg&name=orig)
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
🎉 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
🔥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
🚀 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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