Generalized Embedding Machines for Recommender Systems  

作　　者：Enneng Yang Xin Xin Li Shen Yudong Luo Guibing Guo 

机构地区：[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

出　　处：《Machine Intelligence Research》2024年第3期571-584,共14页机器智能研究（英文版）

基　　金：supported by National Natural Science Foundation of China(Nos.62032013 and 61972078);the Fundamental Research Funds for the Central Universities,China(No.N2217004).

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) 

分 类 号：F713.36[经济管理—产业经济] TP181[自动化与计算机技术—控制理论与控制工程]