Distributionally Robust Graph-based Recommendation System
CoRR(2024)
摘要
With the capacity to capture high-order collaborative signals, Graph Neural
Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS).
However, their efficacy often hinges on the assumption that training and
testing data share the same distribution (a.k.a. IID assumption), and exhibits
significant declines under distribution shifts. Distribution shifts commonly
arises in RS, often attributed to the dynamic nature of user preferences or
ubiquitous biases during data collection in RS. Despite its significance,
researches on GNN-based recommendation against distribution shift are still
sparse. To bridge this gap, we propose Distributionally Robust GNN (DR-GNN)
that incorporates Distributional Robust Optimization (DRO) into the GNN-based
recommendation. DR-GNN addresses two core challenges: 1) To enable DRO to cater
to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing
regularizer, thereby facilitating the nuanced application of DRO; 2) Given the
typically sparse nature of recommendation data, which might impede robust
optimization, we introduce slight perturbations in the training distribution to
expand its support. Notably, while DR-GNN involves complex optimization, it can
be implemented easily and efficiently. Our extensive experiments validate the
effectiveness of DR-GNN against three typical distribution shifts. The code is
available at https://github.com/WANGBohaO-jpg/DR-GNN .
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