STORE: Streamlining Semantic Tokenization and Generative Recommendation within a Single LLM
Qijiong LIU, Jieming ZHU#, Lu FAN, Zhou ZHAO, Xiao-Ming WU
[Code] [Paper]
Vector Quantization for Recommender Systems: A Review and Outlook
Qijiong LIU*, Xiaoyu DONG*, Jiaren XIAO, Nuo CHEN, Hengchang HU, Jieming ZHU, Chenxu ZHU, Tetsuya SAKAI, and Xiao-Ming WU#
*Equal contribution (co-first authors).
[Paper]
Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
May 28th, 2024: Got accepted by KDD ‘24, along with our tutorial Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Tutorial.
Qijiong LIU*, Jieming ZHU*#, Yanting YANG, Quanyu DAI, Zhaocheng DU, Xiao-Ming WU, Zhou ZHAO, Rui ZHANG, Zhenhua DONG
*Equal contribution. Correspondence to: Jieming Zhu.
[Paper]
Discrete Semantic Tokenization for Deep CTR Prediction
Mar. 6th, 2024: Got accepted by TheWebConf ‘24.
Qijiong LIU, Hengchang HU, Jiahao WU, Jieming ZHU, Min-Yen KAN#, Xiao-Ming WU#
[Code] [Paper]
Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision
Dec. 15th, 2023: Got accepted by ECIR ‘24. Congrats to Hengchang!
Hengchang HU, Qijiong LIU, Chuang LI, Min-Yen KAN#
[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]
Jan. 24th, 2024: Got accepted by TheWebConf ‘24.
Qijiong LIU, Lu FAN*, Jiaren XIAO*, Jieming ZHU, and Xiao-Ming WU#
*Equal contribution (co-second 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]
Benchmarking News Recommendation in the Era of Green AI
Mar. 6th, 2024: Got accepted by TheWebConf ‘24. It is a revised and condensed version of the previous work titled Only Encode Once: Making Content-based News Recommender Greener. While the core ideas and results remain consistent, the presentation scope have been modified for brevity and clarity. For the full details and extended discussions, please refer to the original long paper at here.
Qijiong LIU*, Jieming ZHU*, Quanyu DAI, Xiao-Ming WU#
*Equal contribution (co-first authors).
[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]