Research on the Design of Mass Recommendation System Based on Lambda Architecture
DOI:
https://doi.org/10.13052/jwe1540-9589.20614Keywords:
Lambda architecture; Mass; Recommender system; Cascading; Hybrid algorithmAbstract
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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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.
