Condensing Pre-Augmented Recommendation Data via Lightweight Policy Gradient Estimation (Extended Abstract)

Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu, Qijiong Liu, Rui He, Qing Li, Ke Tang

Abstract: Training recommendation models on large datasets is time- and resource-intensive. It is desired to construct concise yet informative datasets for efficient training. Recent advances in dataset condensation offer a promising solution by synthesizing compact datasets. However, existing methods face two key limitations when applied to recommendation: (1) they fail to generate discrete user-item interactions, and (2) they could not preserve users' potential preferences. To address the limitations, we propose a lightweight condensation framework tailored for recommendation (DConRec), focusing on condensing user-item historical interaction sets. Specifically, we model the discrete user-item interactions via a probabilistic approach and design a pre-augmentation module to incorporate the potential user preferences into the condensed datasets. While the substantial size of datasets leads to costly optimization, we propose a lightweight policy gradient estimation to accelerate the data synthesis. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of DConRec. Besides, we theoretically examine the provable convergence of DConRec.

External IDs:dblp:conf/icde/WuFCLLHLT25