Abstract
At present, there are many studies on the recommendation algorithm based on fuzzy theory. However, the recommendation algorithm based on type-2 fuzzy theory has always been a difficult point in theoretical research. The main reason is that the calculation of type-2 fuzzy is highly complex which is difficult to deal with in the actual application process, thus limiting its application in recommendation system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bargiela A, Pedrycz W (2008) Toward a theory of granular computing for human-centered information processing. IEEE Trans Fuzzy Syst 16(2):320–330
Bargiela A, Pedrycz W (2012) Granular computing: an introduction. Springer Science and Business Media, Berlin
Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust optimization. Princeton University Press, Princeton
Bernardo JJ (1977) An assignment approach to choosing RandD experiments. Decis Sci 8(2):489–501
Brauers WKM, Zavadskas EK (2010) Project management by multimoora as an instrument for transition economies. Ukio Technologinis Ir Ekonominis Vystymas 16(1):5–24
Cai JF, Candès EJ, Shen Z (2010) A singular value thresholding algorithm for matrix completion. SIAM J Optim 20(4):1956–1982
Gacek A, Pedrycz W (2015) Clustering granular data and their characterization with information granules of higher type. IEEE Trans Fuzzy Syst 23(4):850–860
Koltchinskii V, Lounici K, Tsybakov AB (2011) Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion. Ann Stat 39(5):2302–2329
Martinez L, Barranco MJ, Pérez LG, Espinilla M (2008) A knowledge based recommender system with multigranular linguistic information. Int J Comput Intell Syst 1(3):225–236
Pedrycz W (2002) Granular computing: an introduction. In: IFSA World Congress and, NAFIPS International Conference Joint, vol 3. IEEE, New York, pp 1349–1354
Pedrycz W (2005) Knowledge-based clustering: from data to information granules. Wiley, Hoboken
Pedrycz W, Bargiela A (2012) An optimization of allocation of information granularity in the interpretation of data structures: toward granular fuzzy clustering. IEEE Trans Syst Man Cyb 42(3):582–590
Pedrycz W, Homenda W (2013) Building the fundamentals of granular computing: a principle of justifiable granularity. Appl Soft Comput 13(10):4209–4218
Pedrycz W, Skowron A, Kreinovich V (2008) Handbook of granular computing. Wiley, Hoboken, pp 719–740
Pedrycz W, Al-Hmouz R, Balamash AS et al (2015) Hierarchical granular clustering: an emergence of information granules of higher type and higher order. IEEE Trans Fuzzy Syst 23(6):2270–2283
Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57
Rezaei J (2016) Best-worst multi-criteria decision-making method: some properties and a linear model. Omega 64:126–130
Vandereycken B (2013) Low-rank matrix completion by Riemannian optimization. SIAM J Optim 23(2):1214–1236
Zadeh LA (1996) Fuzzy logic=computing with words. IEEE Trans Fuzzy Syst 4(2):103–111
Zhang W (2013) A hybrid multi-criteria recommendation system combining fuzzy mathematics and multi-objective decision making methods. University of Electronic Science and Technology of China. (In Chinese)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Qin, J., Liu, X. (2019). Interval Type-2 Fuzzy Decision Making Based on Granular Computing and Its Application in Personalized Recommendation. In: Type-2 Fuzzy Decision-Making Theories, Methodologies and Applications. Uncertainty and Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-13-9891-9_10
Download citation
DOI: https://doi.org/10.1007/978-981-13-9891-9_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9890-2
Online ISBN: 978-981-13-9891-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)