  Collaborative filtering (CF) has exhibited prominent results for recommender
systems and been broadly utilized for real-world applications. A branch of
research enhances CF methods by message passing used in graph neural networks,
due to its strong capabilities of extracting knowledge from graph-structured
data, like user-item bipartite graphs that naturally exist in CF. They assume
that message passing helps CF methods in a manner akin to its benefits for
graph-based learning tasks in general. However, even though message passing
empirically improves CF, whether or not this assumption is correct still needs
verification. To address this gap, we formally investigate why message passing
helps CF from multiple perspectives and show that many assumptions made by
previous works are not entirely accurate. With our curated ablation studies and
theoretical analyses, we discover that (1) message passing improves the CF
performance primarily by additional representations passed from neighbors
during the forward pass instead of additional gradient updates to neighbor
representations during the model back-propagation and (ii) message passing
usually helps low-degree nodes more than high-degree nodes. Utilizing these
novel findings, we present Test-time Aggregation for CF, namely TAG-CF, a
test-time augmentation framework that only conducts message passing once at
inference time. The key novelty of TAG-CF is that it effectively utilizes graph
knowledge while circumventing most of notorious computational overheads of
message passing. Besides, TAG-CF is extremely versatile can be used as a
plug-and-play module to enhance representations trained by different CF
supervision signals. Evaluated on six datasets, TAG-CF consistently improves
the recommendation performance of CF methods without graph by up to 39.2% on
cold users and 31.7% on all users, with little to no extra computational
overheads.
