Data Watermarking for Sequential Recommender Systems

TL;DR

This paper introduces DWRS, a data watermarking method for sequential recommender systems that embeds identifiable sequences into datasets to protect ownership without significantly affecting model performance.

Contribution

The paper proposes a novel dataset watermarking technique specifically designed for sequential recommender systems, addressing both dataset and user data protection.

Findings

DWRS effectively embeds watermarks detectable in trained models.

Watermarks do not significantly degrade recommendation accuracy.

The method is validated across multiple models and datasets.

Abstract

In the era of large foundation models, data has become a crucial component in building high-performance AI systems. As the demand for high-quality and large-scale data continues to rise, data copyright protection is attracting increasing attention. In this work, we explore the problem of data watermarking for sequential recommender systems, where a watermark is embedded into the target dataset and can be detected in models trained on that dataset. We focus on two settings: dataset watermarking, which protects the ownership of the entire dataset, and user watermarking, which safeguards the data of individual users. We present a method named Dataset Watermarking for Recommender Systems (DWRS) to address them. We define the watermark as a sequence of consecutive items inserted into normal users' interaction sequences. We define a Receptive Field (RF) to guide the inserting process to facilitate the memorization of the watermark. Extensive experiments on five representative sequential recommendation models and three benchmark datasets demonstrate the effectiveness of DWRS in protecting data copyright while preserving model utility.