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Generalized Embedding Machines for Recommender Systems

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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 embed-ding 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.

Feature interactionshigh-order interactionfactorization machine(FM)recommender systemgraph neural network(GNN)

Enneng Yang、Xin Xin、Li Shen、Yudong Luo、Guibing Guo

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Software College,Northeastern University,Shenyang 110000,China

School of Computer Science and Technology,Shandong University,Qingdao 266000,China

JD Explore Academy,JD Explore Academy,Beijing 100000,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Central Universities,China

6203201361972078N2217004

2024

10.1007/s11633-022-1412-6

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(3)
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