How Does Message Passing Improve Collaborative Filtering?

Mingxuan Ju, William Shiao, Zhichun Guo, Yanfang Ye, Yozen Liu, Neil Shah, Tong Zhao

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: Collaborative Filtering, Recommender System, Test-time Augmentation

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TL;DR: We investigate the reason behind why message passing helps collaborative filtering and accordingly propose a simple, efficient, and effective test-time augmentation framework (i.e., TAG-CF).

Abstract:

Collaborative filtering (CF) has exhibited prominent results for recommender systems and is broadly utilized for real-world applications. A branch of research enhances CF methods with 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 (e.g., node classification). However, whether or not this assumption is correct still needs verification, even though message passing empirically improves CF. To address this gap, we formally investigate why message passing helps CF from multiple perspectives (i.e., information passed from neighbors, additional gradients for neighbors, and individual improvement gains of subgroups w.r.t. the node degree) and show that many assumptions made by previous works are not entirely accurate. With our rigorously designed ablation studies and analyses, we discover that message passing (i) improves the CF performance primarily by information passed from neighbors instead of their accompanying gradients and (ii) usually helps low-degree nodes more than high-degree nodes. Utilizing these novel findings, we present Test-time Aggregation for Collaborative Filtering, namely TAG-CF, a test-time augmentation framework that only conducts message passing once at inference time.
It can be used as a plug-and-play module and is effective at enhancing representations trained by different CF supervision signals. Evaluated on five datasets, TAG-CF performs on par with or better than trending graph-based CF methods with less than 1% of their total training time. Furthermore, we show that test-time aggregation in TAG-CF improves recommendation performance in similar ways as the training-time aggregation does, demonstrating the legitimacy of our findings on why message passing improves CF.

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Submission Number: 1469