Research on the Design of Mass Recommendation System Based on Lambda Architecture

Authors

  • Bowen Chen School of Electronics and Electrical Engineering, Hubei University of Technology, Hubei, Wuhan, 430068, China
  • Li Zhu Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of technology, Wuhan, 430068, P. R. China
  • Da Wang School of Electronics and Electrical Engineering, Hubei University of Technology, Hubei, Wuhan, 430068, China
  • JunHua Cheng Manager of No. 11 branch of China Communications Construction Third Engineering Bureau Co., Ltd, P. R. China

DOI:

https://doi.org/10.13052/jwe1540-9589.20614

Keywords:

Lambda architecture; Mass; Recommender system; Cascading; Hybrid algorithm

Abstract

In the era of big data, in order to increasing the information data for conforms to the personalized needs of content, research scholars put forward based on the Lambda mass recommendation system architecture design, it can not only to the recessive and dominant behavior of users of the system data collection storage and research analysis, can also be based on the analysis of cascading hybrid algorithm to explore how to carry out real-time recommendation. Therefore, on the basis of understanding the research and development achievements of recommender systems at home and abroad in recent years, and based on the understanding and analysis of Lambda architecture and cascading hybrid algorithm, this paper aims at how to design a massive recommender system in line with users’ behavior, and makes clear the recommendation effect by combining with system testing.

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Author Biographies

Bowen Chen, School of Electronics and Electrical Engineering, Hubei University of Technology, Hubei, Wuhan, 430068, China

Bowen Chen received the B.S. degree in electric engineering and automation from Hubei Polytechnic University, City, China, in 2019. He is currently working toward the M.S. degree in control engineering with the School of Hubei University of Technology, City, China. His research areas include big data, deep learning, and recommendation systems.

Li Zhu, Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of technology, Wuhan, 430068, P. R. China

Li Zhu received the B.S. degree in school of information science and engineering from Wuhan University of Science and Technology, Wuhan, China, in 2004. She received the M.S. degree in Physical Electronics from Huaqiao University, Quanzhou, China, in 2007, and the Ph.D. degree in Communication and Information system from Wuhan University, Wuhan, China, in 2011. She is currently an associate professor in the School of Electrical and Electronic Engineering, Hubei University of Technology, Hubei, China. Her research areas include Artificial Intelligence and Big Data.

Da Wang, School of Electronics and Electrical Engineering, Hubei University of Technology, Hubei, Wuhan, 430068, China

Da Wang received the Master degree in spatial information science and technology from Huazhong University of Science and Technology, Wuhan, China in 2008 and received the Ph.D. degree in 2012. From 2012 to now, he is a lecturer in Electrical and Electronic Engineering of HuBei University of Technology. His research interests include image reconstruction, virtual reality and data mining.

JunHua Cheng, Manager of No. 11 branch of China Communications Construction Third Engineering Bureau Co., Ltd, P. R. China

JunHua Cheng graduated from Hubei University of technology with a bachelor’s degree in management engineering. Senior engineer, national first-class constructor (communication, radio and television), long engaged in communication network construction, operation and maintenance and network optimization.

References

Nielsen T A, Spertus E, Drobychev A. Methods and systems for controlling access to relationship information in a social network[J]. US, 2013.

Adomavicius, Gediminas, Tuzhilin, et al. Using Data Mining Methods to Build Customer Profiles[J]. Computer, 2001.

Bouza L. How could the new article 11 TEU contribute to reduce the EU’s democratic malaise?[J]. Revista Brasileira De Milho E Sorgo, 2018, 13:312–325.

Nikovski D N. Method and system for recommending products to consumers by induction of decision trees[J]. US, 2007.

Yuki, Urabe, Rafal, et al. Emoticon Recommendation System to Richen Your Online Communication[J]. International journal of multimedia data engineering & management, 2014, 5(1):14–33.

Wang J J, Liang Y, Su J T, et al. An Analysis of the Economic Impact of US Presidential Elections Based on Principal Component and Logical Regression[J]. Complexity, 2021, 2021(8):1–12.

Khalifa R M, Yacout S, Bassetto S. Developing machine-learning regression model with Logical Analysis of Data (LAD)[J]. Computers & Industrial Engineering, 2020:106947.

Wang X L, Li L Q, Xie W X. A novel T-S fuzzy particle filtering algorithm based on fuzzy C-regression clustering[J]. International Journal of Approximate Reasoning, 2019, 117.

Gao T, Liu J. Application of improved random forest algorithm and fuzzy mathematics in physical fitness of athletes[J]. Journal of Intelligent and Fuzzy Systems, 2020, 40(4):1–13.

Liu H, Lin H, X Jiang, et al. Estimation of mass matrix in machine tool’s weak components research by using symbolic regression[J]. Computers & Industrial Engineering, 2019, 127(JAN.):998–1011.

Biswas S, Nath S, Dey S, et al. Tangent-cut optimizer on gradient descent: an approach towards Hybrid Heuristics[J]. Artificial Intelligence Review, 2021:1–27.

Fang Z, Guo Z C, Zhou D X. Optimal learning rates for distribution regression[J]. Journal of complexity, 2020, 56(Feb.):101426.1-101426.15.

Ossai C I, Egwutuoha I P. Real-time state-of-health monitoring of lithium-ion battery with anomaly detection, Levenberg–Marquardt algorithm, and multiphase exponential regression model[J]. Neural Computing and Applications, 2020(4).

Kumar P B, Parhi D R. Intelligent Hybridization of Regression Technique with Genetic Algorithm for Navigation of Humanoids in Complex Environments[J]. Robotica, 2020, 38(4):565–581.

Yu X, Liu J, Keung J W, et al. Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM[J]. IEEE Transactions on Reliability, 2019.

Gong C, Wang P H, Su Z G. An interactive nonparametric evidential regression algorithm with instance selection[J]. Soft Computing, 2020, 24(11).

Rueda R, Ruiz L, MP Cuéllar, et al. An Ant Colony Optimization approach for symbolic regression using Straight Line Programs. Application to energy consumption modelling[J]. International Journal of Approximate Reasoning, 2020, 121.

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Published

2021-10-18

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