STORE: Streamlining Semantic Tokenization and Generative Recommendation within a Single LLM
Qijiong LIU, Jieming ZHU#, Lu FAN, Zhou ZHAO, Xiao-Ming WU
[Code] [Paper]
Jiahao WU*, Qijiong LIU*, Hengchang HU, Wenqi FAN, Shengcai LIU, Qing LI#, Xiao-Ming WU#, and Ke TANG#
*Equal contribution (co-first authors)
[Code] [Paper]
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
Aug. 25th, 2024: Got introduced on the Workshop on Generative AI for Recommender Systems and Personalization!
Oct. 20th, 2023: Got accepted by WSDM ‘24. Our first version (i.e., GENRE) discussed the use of closed-source LLMs (e.g., GPT-3.5) in recommender systems, while this version (i.e., ONCE) further combines open-source LLMs (e.g., LLaMA) and closed-source LLMs in recommender systems.
Qijiong LIU, Nuo CHEN, Tetsuya SAKAI, and Xiao-Ming WU#
[Code] [Paper]
A First Look at LLM-powered Generative News Recommendation
Oct. 20th, 2023: The expanded version, i.e., ONCE, got accepted by WSDM ‘24.
Oct. 10th, 2023: Got introduced on DataFunTalk!
Qijiong LIU, Nuo CHEN, Tetsuya SAKAI, and Xiao-Ming WU#
[Code] [Paper]