Abstract
Most collaborative filtering recommendation algorithms use crisp ratings to represent the users’ preferences. However, users’ preferences are subjective and changeable, crisp ratings can’t measure the uncertainty of users’ preferences effectively. In order to solve this problem, this paper proposes the interval-valued triangular fuzzy rating model. This model replaces crisp ratings with interval-valued triangular fuzzy numbers on the basis of users’ rating statistics information, which can measure the users’ preferences in a more reasonable way. Based on this model, the collaborative filtering recommendation algorithm based on interval-valued fuzzy numbers is designed. The algorithm calculates the users’ similarity by the interval-valued triangular fuzzy numbers, and takes the ambiguity of ratings into consideration in the prediction stage. Our experiments prove that, compared with other fuzzy and traditional algorithms, our algorithm can increase the prediction precision and rank accuracy effectively with a little time cost, and has an obvious advantage when implemented in a sparse dataset which has more users than items. Thus our method has strong effectiveness and practicability.
Similar content being viewed by others
References
Bartikowski B, Walsh G (2014) Attitude contagion in consumer opinion platforms: posters and lurkers. Electron Mark 24(3):207–217
Ortega F, Rojo D, Valdiviezo-Diaz P, Raya L (2018) Hybrid collaborative filtering based on users rating behavior. IEEE Access 6:69582–69591
Zhang P, Zhang Z, Tian T, Wang Y (2019) Collaborative filtering recommendation algorithm integrating time windows and rating predictions. Applied Intelligence
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge & Data Engineering 17(6):734–749
Nisha CC, Mohan A (2018) A social recommender system using deep architecture and network embedding. Applied Intelligence
Hangyu Y, Yan T (2019) Collaborative filtering based on gaussian mixture model and improved Jaccard similarity. IEEE Access 7:118690–118701
Yin L, Deng Y (2018) Measuring transferring similarity via local information. Physica A: Statistical Mechanics and its Applications 498:102–115
Breese JS, Heckerman D, Kadie C (2013) Empirical analysis of predictive algorithms for collaborative filtering. Fourteenth Conference on Uncertainty in Artificial Intelligence 7:43–52
Cheng LC, Wang HA (2014) A fuzzy recommender system based on the integration of subjective preferences and objective information. Appl Soft Comput 18(1):290–301
Albadvi A, Shahbazi M (2009) A hybrid recommendation technique based on product category attributes. Expert Syst Appl 36(9):11480–11488
Liu P, Wang Y, Jia F, Fujita H (2020) A multiple attribute decision making three-way model for intuitionistic fuzzy numbers. Int J Approx Reason 119:177–203
Capuano N, Chiclana F, Herrera-Viedma E, Fujita H, Loiae V (2019) Fuzzy group decision making for influence-aware recommendations. Comput Hum Behav 101:371–379
Lu J, Shambour Q, Xu Y, Lin Q, Zhang G (2013) A web-based personalized business partner recommendation system using fuzzy semantic techniques. Comput Intell 29(1):37–69
Chao-Lung Y, Shang-Che H, Kai-Lung H, Wen-Huang C (2019) Fuzzy personalized scoring model for recommendation system. 2019 IEEE international conference on acoustics, speech and signal processing pp 1577–1581
Zhang J, Chen D, Lu M (2018) Combining sentiment analysis with a fuzzy kano model for product aspect preference recommendation. IEEE Access 6:59163–59172
Gorzałczany MB (1987) A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets & Systems 21(1):1–17
Yao JS, Lin FT (2002) Constructing a fuzzy flow-shop sequencing model based on statistical data. Int J Approx Reason 29(1):215–234
Deschrijver G (2007) Arithmetic operators in interval-valued fuzzy set theory. Inform Sci 177(14):2906–2924
Chen TY (2012) Multiple criteria group decision-making with generalized interval-valued fuzzy numbers based on signed distances and incomplete weights. Appl Math Model 36(7):3029–3052
Han Y, Deng Y, Zehong C, Chin-Teng L (2019) An interval-valued pythagorean prioritized operator based game theoretical framework with its applications in multicriteria group decision making. Neural computing and applications
Zadeh LA (1968) Probability measures of fuzzy events. Journal of Mathematical Analysis & Applications 23 (2):421–427
Chen SH (1985) Operations on fuzzy numbers with function principal. Tamkang Journal of Management Sciences 6(1):13– 25
Hong DH, Lee S (2002) Some algebraic properties and a distance measure for interval-valued fuzzy numbers. Inform Sci 148(1):1–10
Lin FT (2002) Fuzzy job-shop scheduling based on ranking level (λ, 1) interval-valued fuzzy numbers. IEEE Trans Fuzzy Syst 10(4):510–522
Xi-Zhi W (2004) Statistics : from data to conclusions. China Statistics Press, Beijing
Huete JF, Fernández-Luna JM, Campos LMD, Rueda-Morales MA (2012) Using past-prediction accuracy in recommender systems. Information Sciences 199(15):78–92
Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl.-Based Syst 56(3):156–166
DÁz M B, Porter MA, Onnela JP (2010) Competition for popularity in bipartite networks. Chaos An Interdisciplinary Journal of Nonlinear Science 20(4)
GroupLens, http://files.grouplens.org/datasets/movielens/ml--100k/
Lü L, Medo M, Chi HY, Zhang YC, Zhang ZK, Zhou T (2012) Recommender systems. Phys Rep 519(3):18–21
Cacheda F, Carneiro V, Fernández D, Formos V (2011) Comparison of collaborative filtering algorithms. Acm Transactions on the Web 5:1–33
Russell S, Yoon V (2008) Applications of wavelet data reduction in a recommender system. Expert Syst Appl 34(4):2316– 2325
Wu Y, Zhang X, Wang X, Li H (2016) User fuzzy similarity-based collaborative filtering recommendation algorithm. Journal on Communications 37(1):198–206
Wu Y, Zhang X, Yu H, Wei S, Guo W (2017) Collaborative filtering recommendation algorithm based on user fuzzy similarity. Intell Data Anal 2:311–327
Lee J, Kim S, Lebanon G, Singer Y, Bengio S (2016) LLORMA: Local low-rank matrix approximation. J Mach Learn Res 17(1):442–465
Sun Z, Guo G, Zhang J (2015) Exploiting implicit item relationships for recommender systems. Proceedings of the 2015 user modeling, adaptation and personalization, pp 252–264
Juan Y, Zhuang Y, Chin WS, Lin CJ (2016) Field-aware factorization machines for CTR prediction. Proceedings of the 10th ACM conference on recommender systems, pp 43–50
Acknowledgments
This work was jointly supported by the National Natural Science Foundation for Creative Research Groups of China(grant numbers 61521003).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wu, Y., ZHao, Y. & Wei, S. Collaborative filtering recommendation algorithm based on interval-valued fuzzy numbers. Appl Intell 50, 2663–2675 (2020). https://doi.org/10.1007/s10489-020-01661-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-020-01661-z