Research on personalized recommendation system to improve overall diversity

Liyan Liu

doi:10.1051/e3sconf/202021802043

All issues

Volume 218 (2020)

E3S Web Conf., 218 (2020) 02043

References

Open Access

Issue		E3S Web Conf. Volume 218, 2020 2020 International Symposium on Energy, Environmental Science and Engineering (ISEESE 2020)


Article Number		02043
Number of page(s)		4
Section		New Energy Development and Energy Sustainable Development Optimization
DOI		https://doi.org/10.1051/e3sconf/202021802043
Published online		11 December 2020

Adomavicius G. and K Won Y, A Treatise on Electricity and Magnetism, Optimization-based approaches for maximizing aggregate recommendation diversity[J], INFORMS Journal on Computing. 2019, (24) :896-911. [2] Nunez-Valdez E. R., Cueva-Lovelle T.M., Sanjuan-Martt’nez O., Garct ’a-Dt’az V., Ordonez P., Montenegro-Man’n C.E., (2012) Implicit feedback techniques on recommender systems applied toelectronic books, Computers in Human Behavior. 2018, 28 (4):1186-1193. [Google Scholar]
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Wang Fuqiang, Peng Furong, Ding Xiaohuan and Lu Jianfeng, “Collaborative filtering recommendation algorithm based on location asymmetric similarity measure, ” J Computer application., 2016, 36(01):171-174. [Google Scholar]
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[1] Adomavicius G. and K Won Y, A Treatise on Electricity and Magnetism, Optimization-based approaches for maximizing aggregate recommendation diversity[J], INFORMS Journal on Computing. 2019, (24) :896-911. [2] Nunez-Valdez E. R., Cueva-Lovelle T.M., Sanjuan-Martt’nez O., Garct ’a-Dt’az V., Ordonez P., Montenegro-Man’n C.E., (2012) Implicit feedback techniques on recommender systems applied toelectronic books, Computers in Human Behavior. 2018, 28 (4):1186-1193. [Google Scholar]

[2] Wang Cheng, Zhu Zhigang, Zhang Yuxia and Su Fangfang, “Improvement of recommendation efficiency and personalization of user based collaborative filtering algorithm, ” Minicomputer system, 2016(03):428-432 [Google Scholar]

[3] Wang Fuqiang, Peng Furong, Ding Xiaohuan and Lu Jianfeng, “Collaborative filtering recommendation algorithm based on location asymmetric similarity measure, ” J Computer application., 2016, 36(01):171-174. [Google Scholar]

[4] Zhu Jialong, Lv Yuqin and Li Weihai, “A new collaborative filtering algorithm based on asymmetric Pearson similarity, ” [EB/OL] Beijing: China Scientific Papers Online http://www.paper.edu.cn/releasepaper/content/, 2014 01-394. [Google Scholar]

[5] Jiang Shuhao, Zhang Liyi and Zhou Na, “Collaborative filtering recommendation based on dynamic change of user preferences” [J], Computer and modernization, 2020(1). [Google Scholar]