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.
|Title of host publication||WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||9|
|Publication status||Published - 2022 Feb 11|
|Event||15th ACM International Conference on Web Search and Data Mining, WSDM 2022 - Virtual, Online, United States|
Duration: 2022 Feb 21 → 2022 Feb 25
|Name||WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining|
|Conference||15th ACM International Conference on Web Search and Data Mining, WSDM 2022|
|Period||22/2/21 → 22/2/25|
Bibliographical noteFunding Information:
The work of Sang-Wook Kim was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-IT1901-03. The work of Noseong Park was supported by the Yonsei University Research Fund of 2021 and the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)).
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications