2022

Personalized Prompt for Sequential Recommendation

Wu, Yiqing, Xie, Ruobing, Zhu, Yongchun et al.

Understand

Pre-training models have shown their power in sequential recommendation.

Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to more effectively and 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 in recommendation (i.e., items) do not have explicit explainable semantics, and the sequence modeling should be personalized.
In this work, we first introduces prompt to recommendation and propose a novel Personalized prompt-based recommendation (PPR) framework for cold-start recommendation.

Built on

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