91 citations · 96 across the 13 of their papers we have counts for
8 papers · 1 filter
Learning Robust Personalized Prompts for LLM-Driven Sequential Recommendation
Xiaolin Zheng, Qiyong Zhong, Jiajie Su +1
LLM-driven sequential recommendation formulates next-item prediction as autoregressive generation conditioned on natural-language prompts. However, minor wording changes in semanti…
Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation
Huwei Ji, Jiajie Su, Yuyuan Li +2
LLM-based Cross-Domain Sequential Recommendation (CDSR) leverages LLMs to enhance target performance via deep semantic reasoning, alleviating the dependency on overlapping users. A…
An item is worth one token in Multimodal Large Language Models-based Sequential Recommendation
Qiyong Zhong, Jiajie Su, Ming Yang +3
Sequential recommendations (SR) predict users' future interactions based on their historical behavior. The rise of Large Language Models (LLMs) has brought powerful generative and…
Pctx: Tokenizing Personalized Context for Generative Recommendation
Qiyong Zhong, Jiajie Su, Yunshan Ma +2
Generative recommendation (GR) models tokenize each action into a few discrete tokens (called semantic IDs) and autoregressively generate the next tokens as predictions, showing ad…
Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation
Jiajie Su, Qiyong Zhong, Yunshan Ma +5
Session-based recommendation (SBR) predicts the next item based on anonymous sessions. Traditional SBR explores user intents based on ID collaborations or auxiliary content. To fur…
Enhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation
Xinping Zhao, Chaochao Chen, Jiajie Su +2
Session-based Recommendation (SBR), seeking to predict a user's next action based on an anonymous session, has drawn increasing attention for its practicability. Most SBR models on…
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation
Jiajie Su, Chaochao Chen, Zibin Lin +3
Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interacti…
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation
Xiaolin Zheng, Jiajie Su, Weiming Liu +1
Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by modeling how users transit among items. However, the short interaction sequences limit the perfo…