Abstract
As an effective way to solve information overload, the collaborative filtering (CF) algorithm has been widely used in the personalized recommendation. In order to improve the accuracy of recommendation, an improved collaborative recommendation algorithm is proposed. Firstly, evaluate the user’s judging power based on historical scoring; then, combine the user’s judging power and similarity to improve the traditional user-based CF algorithm. Experimental results show that the judging power is positively correlated with the recommendation abilities of users and also verify that the judging power extracts the depth information from historical scoring and factors to influence a user on adopting the recommendation results.
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References
Cacheda F, Carneiro V, Fernández D, et al. Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web. 2011;5(1):2–33.
Zhang L, Teng P-Q, Qin T. Using key users of social network to solve cold start problem in collaborative recommendation systems. Inf Technol J. 2013;12(22):7004–8.
Liu NN, Zhao M, Xiang E, et al. Online evolutionary collaborative filtering. Fourth ACM Conf. on Recommender Systems, ACM, New York; 2010. p. 95–102.
Zhang Y-F, Chen C, Yu. N-H. Dynamic reordering within the nearest neighbor-based algorithm for collaborative filtering. J Chin Comput Syst. 2011;32(8):1581–6 (In Chinese).
Sun S-H, Kong G-S, Zhao C-W. Collaborative filtering methods based on user relevance degree and weights of recommend-items. 2011 International Conference on Multimedia Technology, IEEE; 2011. p. 5322–5.
Cheng Z, Zhao X-F, Wang J-W. An item-targeted user similarity method for data service recommendation. 2012 I.E. 16th International Enterprise Distributed Object Computing Conference Workshops, IEEE; 2012. p. 172–8.
Zitouni H, Berkani L, Nouali O. Recommendation of learning resources and users using an aggregation-based approach. 2012 Second International Workshop on Advanced Information Systems for Enterprises, IEEE; 2012. p. 57–63.
Zhang L, Teng P-Q, Qin T. An improved collaborative filtering algorithm based on user interest. J Softw. 2014;9(4):999–1006.
Yuan Q, Zhao S, Chen L, et al. Augmenting collaborative recommender by fusing explicit social relationships. In ACM RecSys‘09 Workshop on Recommender Systems and the Social Web, ACM New York; 2009. p. 49–56.
Lee DH, Brusilovsky P, Schleyer T. Recommending collaborators using social features and mesh terms. Proc Am Soc Inf Sci Technol. 2011;48(1):1–10.
Chen W, Fong S. Social network collaborative filtering framework and online trust factors: a case study on Facebook. 2010 Fifth International Conference on Digital Information Management (ICDIM), IEEE; 2010. p. 266–73.
Zheng R, Provost F, Ghose A. Social network collaborative filtering: preliminary results. Proceedings of the Sixth Workshop on eBusiness (WEB2007), Montreal; 2007. p. 47–55.
Zhou T, Ren J, Medo M, et al. Bipartite network projection and personal recommendation. Phys Rev E. 2007;76(4):046115.
Yang X-Y, Jiong Y, Ibeahim T, et al. Collaborative filtering model fusing singularity and diffusion process. J Softw. 2013;24(8):1868–84 (In Chinese).
Bansal HS, Voyer PA. Word-of-mouth processes within a services purchase decision context. J Serv Res. 2000;3(2):166–77.
Yu Y-K, Zhang Y-C, Laureti P, et al. Decoding information from noisy, redundant, and intentionally distorted sources. Physica A. 2006;371(2):732–44.
Acknowledgments
This work has been supported by the National Social Science Foundation of P. R. China (no. 13BTQ027).
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Zhang, L., Xue, Y., Cao, S. (2015). Combination of User’s Judging Power and Similarity for Collaborative Recommendation Algorithm. In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-11104-9_25
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DOI: https://doi.org/10.1007/978-3-319-11104-9_25
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