  Recommender systems aim to provide personalized services to users and are
playing an increasingly important role in our daily lives. The key of
recommender systems is to predict how likely users will interact with items
based on their historical online behaviors, e.g., clicks, add-to-cart,
purchases, etc. To exploit these user-item interactions, there are increasing
efforts on considering the user-item interactions as a user-item bipartite
graph and then performing information propagation in the graph via Graph Neural
Networks (GNNs). Given the power of GNNs in graph representation learning,
these GNNs-based recommendation methods have remarkably boosted the
recommendation performance. Despite their success, most existing GNNs-based
recommender systems overlook the existence of interactions caused by unreliable
behaviors (e.g., random/bait clicks) and uniformly treat all the interactions,
which can lead to sub-optimal and unstable performance. In this paper, we
investigate the drawbacks (e.g., non-adaptive propagation and non-robustness)
of existing GNN-based recommendation methods. To address these drawbacks, we
introduce a principled graph trend collaborative filtering method and propose
the Graph Trend Filtering Networks for recommendations (GTN) that can capture
the adaptive reliability of the interactions. Comprehensive experiments and
ablation studies are presented to verify and understand the effectiveness of
the proposed framework. Our implementation based on PyTorch is available at
https://github.com/wenqifan03/GTN-SIGIR2022.
