Timeliness of Materials on Reading Recommendation System

Yanling Li, Sokchoo Ng

Abstract


An improved fuzzy logic recommendation method named TFLRS is presented in this paper. The timeliness of reading materials is focused. The upload time of reading materials is attached as an important input parameter, and the numeric weights of input factors are further revised. The experiment result demonstrates that the recommendation ranking order of the latest and the out-of-date reading materials has obviously improved in comparison to the previous FLRS method. It solves the problem that the new reading materials cannot be timely discovered but the out-of-date reading materials always in the front of the recommendation ranking. The timeliness of reading materials effectively guarantees the user preferred newer materials are always at the higher level than the older materials in the recommendation ranking result and the accuracy of reading recommendation system has significantly improved.

Keywords


Timeliness; Recommendation method; Fuzzy Logic; Fuzzy set; Membership function

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References


J. Lian, “Research on User Features Based Daa Minging in Social Networks,” 2013.

J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,” Decis. Support Syst., vol. 74, pp. 12–32, 2015.

M. Salehi, I. Nakhai Kamalabadi, and M. B. Ghaznavi Ghoushchi, “Personalized recommendation of learning material using sequential pattern mining and attribute based collaborative filtering,” Educ. Inf. Technol., pp. 1–23, 2012.

A. Elkahky, “A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems,” pp. 278–288, 2015.

J. N. K. Liu and V. W. S. Cho, “A Hybrid Algorithm for Recommendation Twitter Peers ∗,” pp. 644–649, 2014.

J. Bian, B. Long, L. Li, T. Moon, A. Dong, and Y. Chang, “Exploiting User Preference for Online Learning in Web Content Optimization Systems,” Yichang-Cs.Com, vol. V, no. 212, pp. 1–23, 2014.

Y. Li et al., “Design of a Reading Recommendation Method Based on User Preference for Online Learning,” no. 4, pp. 519–522, 2015.

S. Chaudhari, M. Patil, and J. Bambhori, “Study and Review of Fuzzy Inference Systems for Decision Making and Control,” Am. Int. J. Res. Sci. Technol. Eng. Math., pp. 88–92, 2014.

A. Sinhal and B. Verma, “A novel fuzzy based approach for effort estimation in software development,” ACM SIGSOFT Softw. Eng. Notes, vol. 38, no. 5, p. 1, 2013.

M. Sah and V. Wade, “Automatic mining of cognitive metadata using fuzzy inference,” Proc. 22nd ACM Conf. Hypertext hypermedia - HT ’11, p. 37, 2011.

H. Wan and G. W. Kuriger, “an Intelligent Decision Support System for,” pp. 52–55, 2011.

M. H. Hasan, J. Jaafar, and M. F. Hassan, “Fuzzy-based Clustering of Web Services’ Quality of Service: A Review,” J. Commun., vol. 9, no. 1, pp. 81–90, 2014.

P. Kumar and Y. Singh, “Assessment of Software Testing Time Using Soft Computing Techniques,” SIGSOFT Softw. Eng. Notes, vol. 37, no. 1, pp. 1–6, 2012.

M. S. Iraji, H. Maghamnia, and M. Iraji, “Web Pages Retrieval with Adaptive Neuro Fuzzy System based on Content and Structure,” Int. J. Mod. Educ. Comput. Sci., vol. 7, no. 8, pp. 69–84, 2015.

D. Wu, J. Lu, and G. Zhang, “A Fuzzy Tree Matching-based Personalized e-Learning Recommender System,” IEEE Trans. Fuzzy Syst., vol. 6706, no. c, pp. 1–1, 2014.

S.-M. Chen, S.-H. Cheng, and T.-E. Lin, “Group decision making systems using group recommendations based on interval fuzzy preference relations and consistency matrices,” Inf. Sci. (Ny)., vol. 298, pp. 555–567, 2015.

K. Lin and C. Chiu, “A Fuzzy Similarity Matching Model for Interior Design Drawing Recommendation,” vol. 1, pp. 2–7, 2015.

H. Pandey, “A Fuzzy Logic based Recommender System for E- Learning System with Multi-Agent Framework,” vol. 122, no. 17, pp. 18–21, 2015.

M. K. Muyeba and L. Han, “Fuzzy classification in web usage mining using fuzzy quantifiers,” Proc. 2013 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Min. - ASONAM ’13, pp. 1381–1386, 2013.

X. Luo, X. Yang, C. Jiang, and X. Ban, “Timeliness online regularized extreme learning machine,” Int. J. Mach. Learn. Cybern., 2016.

Y. Gu, J. Liu, Y. Chen, X. Jiang, and H. Yu, “Neurocomputing TOSELM : Timeliness Online Sequential Extreme Learning Machine,” Neurocomputing, vol. 128, pp. 119–127, 2014.

M. Han, M. Yan, Z. Cai, and Y. Li, “Journal of Network and Computer Applications An exploration of broader in fl uence maximization in timeliness networks with opportunistic selection,” J. Netw. Comput. Appl., vol. 63, pp. 39–49, 2016.

C. Kong, “Optimizing Social Connections for Efficient Information Acquisition,” 2016.

S. Ranganath, S. Wang, X. Hu, J. Tang, and H. Liu, “Seeking in Social Media,” vol. 29, no. 10, pp. 2197–2209, 2017.

M. S. Islam, “AN ASSESSMENT FOR FOCUSING THE CHANGE OF DATA QUALITY ( DQ ) WITH TIMELINESS IN INFORMATION MANUFACTURING SYSTEM ( IMS ),” 2013.

Z. Jinfeng, Y. Kan, W. Heng, M. Shaojie, and Z. Jieyong, “Timeliness Optimization Model For Network C 4 ISR System Structure Based on Information Flow,” no. Asei, pp. 745–749, 2015.

F. Zhang, Q. Liu, and A. Zeng, “Timeliness in recommender systems,” Expert Syst. Appl., vol. 85, pp. 270–278, 2017.




DOI: http://dx.doi.org/10.18517/ijaseit.8.1.4024

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