The Solution of Cold Start Problem in Recommender System Introduction

Yuding Liu

doi:10.47297/taposatWSP2633-456908.20230401

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The Solution of Cold Start Problem in Recommender System Introduction

4卷第1期

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The Solution of Cold Start Problem in Recommender System Introduction
The Solution of Cold Start Problem in Recommender System Introduction
International school, Wuhan University of Science and Technology,Hubei,Wuhan,P.R. China,420000
2023年4卷第1期页码：48-54
纸质出版日期： 2023 ，
DOI：10.47297/taposatWSP2633-456908.20230401
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(2023). The Solution of Cold Start Problem in Recommender System Introduction. 科技理论与实践（英文版）, 4(1), 48-54.

YUDING LIU. (2023). The Solution of Cold Start Problem in Recommender System Introduction. Theory and Practice of Science and Technology, 4(1), 48-54..
(2023). The Solution of Cold Start Problem in Recommender System Introduction. 科技理论与实践（英文版）, 4(1), 48-54. DOI： 10.47297/taposatWSP2633-456908.20230401.

YUDING LIU. (2023). The Solution of Cold Start Problem in Recommender System Introduction. Theory and Practice of Science and Technology, 4(1), 48-54.. DOI： 10.47297/taposatWSP2633-456908.20230401.

摘要

Abstract

As big data time is coming

it is harder for us to find the information we need. In this situation

the recommendation systems are getting more attention. Collaborative filtering

matrix segmentation

graph neural network or several kinds of mixed use and other algorithms are applied to the recommendation system and achieved quite good results. However

most recommendation systems are faced with the cold start problem caused by insufficient data of new users. The cold start problem is very important

especially when the new system is born

all users of the system lack data

which makes the cold start problem particularly prominent. This study summarizes the various methods currently used to solve the cold start problem of recommender systems

makes recommendations for the characteristics of each method

and explores the potential impact of the combination of these methods on improving the performance of recommender systems. The innovation of this study lies in the comprehensive and in-depth analysis of the characteristics of different methods

and the suggestions for their combination

which provides new ideas and methods for solving the cold start problem.

关键词

Keywords

Recommender systemsCold startedData sparsity

references

Zhang, S., Yao, L.N., Sun, A.X., et al. (2019). Deep learning based recommender system: a survey and new perspectives. ACM Computing Surveys, 52(1):1-38.[2] Qin, C., Zhu, H.S., Zhuang, F.Z., et al. (2020). A survey on knowledge graph-based recommender systems. Sci Sin Inform, 50: 937-56.[3] Lei, Q.Y. (2019). Research on Cold Start problem in Personalized Recommender system. China Electronic Publishing House, Beijing.[4] Tahmasebi, F., Meghdadi, M., Ahmadian, S., et al. (2021). A hybrid recommendation system based on profile expansion technique to alleviate cold start problem. Multimedia Tools and Applications, 80 (2): 2339-54.[5] Formoso,V., Fernández,D., Cacheda,F.,et al. (2012). Using profile expansion techniques to alleviate the new user problem. Information Processing & Management, 49 (3): 659-72.[6] Xu, J., Zhang, Z., Du, X.X., et al. (2021). Research on collaborative filtering cold start recommendation algorithm based on item semantics. Journal of Chinese Computer Systems. 42 (11):2247-51.[7] Wu, J., Xie, H., Jiang, H. (2022). Survey of graph neural network in recommendation system. Journal of Frontiers of Computer Science and Technology, 16(10):2250-63.[8] Gao, W., Zhu, F., Li, D.Z., et al. (2022). Realization of graph neural network in cold start recommendation. Computer Engineering and Design, 43(9):2558-66.[9] Rjoub,G., Bentahar,J., Cohen,R., et al. (2022). Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems. Information Sciences, 601: 189-206.[10] Jeevamol, J., Renumol, V.G. (2021). An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem. Education and Information Technologies, 26:4993-5022.[11] Feng, J.M., Zhao, Q.X., Xiao, Y.F., et al. (2021). RBPR: A hybrid model for the new user cold start problem in recommender systems. Knowledge-Based Systems, 214:28.[12] Yang, X.D. (2022). Cold started recommender system based on meta learning, https://zhuanlan.zhihu.com/p/361175558.[13] Yu, M., He, W.T., Zhou, X.C., et al. (2022). Review of recommendation system. Journal of Computer Applications, 42(6) : 1898-1913.

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