.png){kind=icon}
返回
Personalized Prompt for Sequential Recommendation
.png){kind=icon}
OA
AI
DOI：10.1109/TKDE.2024.3357498.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning after pre-training in NLP, which helps to more effectively and parameter-efficiently extract useful knowledge from pre-training models for downstream tasks, especially in cold-start scenarios. However, it is challenging to bring prompt-tuning from NLP to recommendation, since the tokens of recommendation (i.e., items) are million-level and do not have concrete explainable semantics, and the sequence modeling in recommendation should be personalized. In this work, we first introduce prompt to recommendation models and propose a novel Personalized prompt-based recommendation (PPR) framework for cold-start recommendation. Specifically, we build personalized soft prompt via a prompt generator based on user profiles, and enable a sufficient training on prompts via a new prompt-oriented contrastive learning. PPR is effective, parameter-efficient, and universal in various tasks. In both few-shot and zero-shot recommendation tasks, PPR models achieve significant improvements over baselines in three large-scale datasets. We also verify PPR's universality in adopting different recommendation models as the backbone. Finally, we explore and confirm the capability of PPR on other tasks such as cross-domain recommendation and user profile prediction, shedding lights on the promising future directions of better using large-scale pre-trained recommendation models.
Keyword:
Task analysis
Behavioral sciences
Training
Tuning
Self-supervised learning
Predictive models
Generators
Contrastive learning
pre-training
prompt
recommender system
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
Treatment of variable valence in organic nomenclature (lambda convention) (Recommendations 1983)
OAAI
OAAI分享收藏Effects of involuntary auditory attention on visual task performance and brain activity.png)
NeuroReport
NeuroReportIF0
PREAI
分享收藏
查看更多内容