Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation

TL;DR

This paper introduces DPCPL, a novel pre-training and prompt-tuning framework for multi-behavior sequential recommendation that effectively filters noise and enhances efficiency, outperforming existing methods on real-world datasets.

Contribution

It presents the first pre-training and prompt-tuning paradigm specifically designed for multi-behavior recommendation, incorporating a noise-filtering mechanism and personalized prompt learning.

Findings

DPCPL achieves superior recommendation accuracy compared to state-of-the-art methods.

The proposed Efficient Behavior Miner effectively filters noise in multi-behavior data.

DPCPL requires minimal parameter tuning and maintains high efficiency.

Abstract

In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit semantic relationships between these behaviors to enhance recommendation performance. However, these methods often entail high computational complexity. To address concerns regarding efficiency, pre-training presents a viable solution. Its objective is to extract knowledge from extensive pre-training data and fine-tune the model for downstream tasks. Nevertheless, previous pre-training methods have primarily focused on single-behavior data, while multi-behavior data contains significant noise. Additionally, the fully fine-tuning strategy adopted by these methods still imposes a considerable computational burden. In response to this challenge, we propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. Specifically, in the pre-training stage, we commence by proposing a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales, thereby facilitating the comprehension of the contextual semantics of multi-behavior sequences. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module, which generates personalized, progressive, and diverse prompts to fully exploit the potential of the pre-trained model effectively. Extensive experiments on three real-world datasets have unequivocally demonstrated that DPCPL not only exhibits high efficiency and effectiveness, requiring minimal parameter adjustments but also surpasses the state-of-the-art performance across a diverse range of downstream tasks.