CASIA OpenIR  > 学术期刊  > Machine Intelligence Research
Generalized Embedding Machines for Recommender Systems
Enneng Yang1; Xin Xin2; Li Shen3; Yudong Luo1; Guibing Guo1
Source PublicationMachine Intelligence Research
AbstractFactorization machine (FM) is an effective model for feature-based recommendation that utilizes inner products to capture second-order feature interactions. However, one of the major drawbacks of FM is that it cannot capture complex high-order interaction signals. A common solution is to change the interaction function, such as stacking deep neural networks on the top level of FM. In this work, we propose an alternative approach to model high-order interaction signals at the embedding level, namely generalized embedding machine (GEM). The embedding used in GEM encodes not only the information from the feature itself but also the information from other correlated features. Under such a situation, the embedding becomes high-order. Then we can incorporate GEM with FM and even its advanced variants to perform feature interactions. More specifically, in this paper, we utilize graph convolution networks (GCN) to generate high-order embeddings. We integrate GEM with several FM-based models and conduct extensive experiments on two real-world datasets. The results demonstrate significant improvement of GEM over the corresponding baselines.
KeywordFeature interactions, high-order interaction, factorization machine (FM), recommender system, graph neural network (GNN)
Citation statistics
Document Type期刊论文
Collection学术期刊_Machine Intelligence Research
Affiliation1.Software College, Northeastern University, Shenyang 110000, China
2.School of Computer Science and Technology, Shandong University, Qingdao 266000, China
3.JD Explore Academy, JD Explore Academy, Beijing 100000, China
Recommended Citation
GB/T 7714
Enneng Yang,Xin Xin,Li Shen,et al. Generalized Embedding Machines for Recommender Systems[J]. Machine Intelligence Research,2024,21(3):571-584.
APA Enneng Yang,Xin Xin,Li Shen,Yudong Luo,&Guibing Guo.(2024).Generalized Embedding Machines for Recommender Systems.Machine Intelligence Research,21(3),571-584.
MLA Enneng Yang,et al."Generalized Embedding Machines for Recommender Systems".Machine Intelligence Research 21.3(2024):571-584.
Files in This Item: Download All
File Name/Size DocType Version Access License
MIR-2022-10-307.pdf(1617KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Enneng Yang]'s Articles
[Xin Xin]'s Articles
[Li Shen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Enneng Yang]'s Articles
[Xin Xin]'s Articles
[Li Shen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Enneng Yang]'s Articles
[Xin Xin]'s Articles
[Li Shen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: MIR-2022-10-307.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.