Generalized Embedding Machines for Recommender Systems

doi:10.1007/s11633-022-1412-6

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	Generalized Embedding Machines for Recommender Systems
	Enneng Yang 1; Xin Xin 2; Li Shen 3; Yudong Luo 1; Guibing Guo 1
发表期刊	Machine Intelligence Research
ISSN	2731-538X
	2024
卷号	21 期号:3 页码:571-584
摘要	Factorization 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.
关键词	Feature interactions, high-order interaction, factorization machine (FM), recommender system, graph neural network (GNN)
DOI	10.1007/s11633-022-1412-6
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/56483
专题	学术期刊_Machine Intelligence Research
作者单位	1.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
推荐引用方式 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.

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