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The Frontiers of Society, Science and Technology, 2023, 5(16); doi: 10.25236/FSST.2023.051608.

Causal Inference-based Study Abroad Program Recommendation System

Author(s)

Qianyi Hou1, Shuyuan Bao2, Longxi Wang3, Zhengxi Hou4, Tianyuan Zhang5

Corresponding Author:
Qianyi Hou
Affiliation(s)

1Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China

2Beijing No. 55 High School, Beijing, China

3Beijing Huijia Private School, Beijing, China

4Tianjin WeiMing School, Xiqing, Tianjin, China

5Beijing New Essence, Beijing, China

Abstract

In recent years, studying abroad has become an important way for more and more people to achieve their personal development goals. However, with a wide range of study abroad programs and varying standards, choosing a suitable study abroad program has become a challenge. This website proposes a recommendation system for studying abroad projects based on causal reasoning. This website conducts causal analysis and inference of user basic information to generate appropriate decisions. Predicting and inferring conclusions based on causal reasoning will lead to more precise planning actions, enabling international students to choose their study abroad projects more accurately, thereby improving the effectiveness of studying abroad. This website is based on a causal reasoning algorithm for career/future planning goals and has designed a corresponding recommendation system for study abroad projects, providing a scientific and reliable way for study abroad planners to choose their study abroad projects.

Keywords

causal inference, recommendation algorithm, SCM

Cite This Paper

Qianyi Hou, Shuyuan Bao, Longxi Wang, Zhengxi Hou, Tianyuan Zhang. Causal Inference-based Study Abroad Program Recommendation System. The Frontiers of Society, Science and Technology (2023) Vol. 5, Issue 16: 40-45. https://doi.org/10.25236/FSST.2023.051608.

References

[1] Huang Guangqiu, Jin Feng, Peng Xuyou. A collaborative filtering commodity recommendation system model based on interest level [J]. Microelectronics & Computer, 2005(3):5-8.

[2] Zhang Guangqian, Bai Xue. A new product recommendation method based on consumption personality [J]. Management Science, 2015(2):60-68.

[3] Huang Hong, Yang Zhuojun, Wang Ben. Application of fuzzy logic in e-commerce product recommendation system [J]. Computer System Applications, 2012(3):171-175.

[4] Xie Yi, Chen Deren, Gan Honghua. A real-time product recommendation method based on browsing preference mining [J]. Computer Applications, 2011(1):89-92.

[5] Huang Hongkun, Tang Jihua, Tong Wencan. Design and implementation of a job recommendation system based on Mahout [J]. Journal of Longyan University, 2019(5):21-26.

[6] Ren Ranran. Research on AI company job recommendation based on data mining [J]. Value Engineering, 2017(34):42-44.

[7] Wang Chao. Architecture and implementation of a job recommendation system based on social relations [J]. Digital Technology & Application, 2013(11):123-125, 127.

[8] Zhang Fuguo. Overview of personalized project recommendation system research based on tags [J]. Journal of Information, 2012(9):963-972.

[9] Yu Li, Liu Lu, Li Xuefeng. Research on personalized recommendation algorithms under multiple interests of users [J]. Computer Integrated Manufacturing System, 2004(12):1610-1615.

[10] Huang Guoyan, Li Youchao, Gao Jianpei, et al. A user clustering collaborative filtering recommendation algorithm based on item attributes [J]. Computer Engineering & Design, 2010(5): 1038-1041.