Linear, or Non-Linear, That is the Question!

TL;DR

This paper introduces HMLET, a hybrid GCN-based collaborative filtering method combining linear and non-linear propagation with a gating mechanism, achieving superior accuracy and revealing correlations between node importance and propagation type.

Contribution

It proposes the first hybrid linear/non-linear GCN model for recommender systems with a gating module and analyzes the relationship between node centrality and propagation type.

Findings

HMLET achieves state-of-the-art accuracy on benchmark datasets.

Important nodes tend to prefer non-linear propagation.

A strong correlation exists between node centrality and propagation choice.

Abstract

There were fierce debates on whether the non-linear embedding propagation of GCNs is appropriate to GCN-based recommender systems. It was recently found that the linear embedding propagation shows better accuracy than the non-linear embedding propagation. Since this phenomenon was discovered especially in recommender systems, it is required that we carefully analyze the linearity and non-linearity issue. In this work, therefore, we revisit the issues of i) which of the linear or non-linear propagation is better and ii) which factors of users/items decide the linearity/non-linearity of the embedding propagation. We propose a novel Hybrid Method of Linear and non-linEar collaborative filTering method (HMLET, pronounced as Hamlet). In our design, there exist both linear and non-linear propagation steps, when processing each user or item node, and our gating module chooses one of them, which results in a hybrid model of the linear and non-linear GCN-based collaborative filtering (CF). The proposed model yields the best accuracy in three public benchmark datasets. Moreover, we classify users/items into the following three classes depending on our gating modules' selections: Full-Non-Linearity (FNL), Partial-Non-Linearity (PNL), and Full-Linearity (FL). We found that there exist strong correlations between nodes' centrality and their class membership, i.e., important user/item nodes exhibit more preferences towards the non-linearity during the propagation steps. To our knowledge, we are the first who design a hybrid method and report the correlation between the graph centrality and the linearity/non-linearity of nodes. All HMLET codes and datasets are available at: https://github.com/qbxlvnf11/HMLET.