Abstract
The primary objective of this research is to search and sort audio gadget list according to user preference with fuzzy logic. The system has a database that contains the price of the product and necessary information along with reviews. This information is considered as the parameters of the fuzzy system. In this system, the user can specify approximate budget, genres, sound quality, and review of the audio gadget that are used to purchase proper audio devices. Each item’s metadata is converted into a fuzzy parameter and passed to fuzzy recommendation system. After calculation, the fuzzy recommendation system assign’s a value to each item. Those values are sorted in descending order, and the highest value is counted as the most preferable item for the user. The system also allows users to give feedback for different attributes such as genre, sound quality, user satisfaction as the review. Therefore, the system becomes user-adaptive when a user uses the system for a while.
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
Park, H.-S., Yoo, J.-O., Cho, S.-B.: A context-aware music recommendation system using fuzzy Bayesian networks with utility theory. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) FSKD 2006. LNCS, vol. 4223, pp. 970–979. Springer, Heidelberg (2006)
Cao, Y., Li, Y.: An intelligent fuzzy-based recommendation system for consumer electronic products, 4 May 2006. https://www.sciencedirect.com/science/article/pii/S0957417406001369
Zhao, N., Wang, Q.H., Zhong, J.F.: Research on fuzzy intelligent recommendation system based on consumer online reviews. In: Wang, M. (ed.) KSEM 2013. LNCS, vol. 8041, pp. 173–183. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39787-5_14
Jang, J.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993). https://doi.org/10.1109/21.256541
Sun, C.: Rule-base structure identification in an adaptive-network-based fuzzy inference system. IEEE Trans. Fuzzy Syst. 2(1), 64–73 (1994). https://doi.org/10.1109/91.273127
Jang, J., Sun, C., Mizutani, E.: Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence [books in brief]. IEEE Trans. Neural Netw. 8(5), 1219 (1997). https://doi.org/10.1109/tnn.1997.623228
Wang, L.: Neural networks and fuzzy systems. IEEE Trans. Fuzzy Syst. 1(2), 146–155 (1993). https://doi.org/10.1109/91.227383
Yager, R.: Fuzzy logic methods in recommender systems, 4 June 2002. https://www.sciencedirect.com/science/article/pii/S0165011402002233
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chowdhury, M.M., Tanvir, F., Rahman, M.S., Rahman, M.M., Al-Sahariar, M., Rahman, R.M. (2019). Audio Gadget Recommendation by Fuzzy Logic. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-19810-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19809-1
Online ISBN: 978-3-030-19810-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)