Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
This addresses the issue of model misspecification in real recommendation systems, offering an incremental improvement by combining model-agnostic and model-based subsampling methods.
The paper tackles the problem of data subsampling for recommendation systems by proposing a model-agnostic method based on graph conductance to estimate interaction importance, achieving superior results on KuaiRec and MIND datasets compared to baselines.
Data subsampling is widely used to speed up the training of large-scale recommendation systems. Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e.g. sample hardness. However, when the pilot model is misspecified, model-based subsampling methods deteriorate. Since model misspecification is persistent in real recommendation systems, we instead propose model-agnostic data subsampling methods by only exploring input data structure represented by graphs. Specifically, we study the topology of the user-item graph to estimate the importance of each user-item interaction (an edge in the user-item graph) via graph conductance, followed by a propagation step on the network to smooth out the estimated importance value. Since our proposed method is model-agnostic, we can marry the merits of both model-agnostic and model-based subsampling methods. Empirically, we show that combing the two consistently improves over any single method on the used datasets. Experimental results on KuaiRec and MIND datasets demonstrate that our proposed methods achieve superior results compared to baseline approaches.