Building a Recommender System Using Collaborative Filtering Algorithms and Analyzing its Performance

Author:

Jeejoe Akash1,Harishiv V.1,Venkatesh Pranay1,Sangeetha S.K.B.1

Affiliation:

1. SRM Institute of Science and Technology

Abstract

Recommender Systems (RS) systems help users to select items and recommend useful items to target customers who are interested in them, such as movies, music, books, and jokes. Traditional recommendation algorithms are primarily concerned with improving performance accuracy; as a result, these algorithms prefer to promote only popular products. Variability is also an important inaccurate number of personalized recommendations that suggest unfamiliar or different things. Multi objective development strategies, which magnify these contradictory measures simultaneously, are used to measure accuracy and variability. Existing algorithms have an important feature because they are not flexible enough to control competing targets. We suggest creating a recommendation system based on shared filtering. Instead of finding out the preferences and preferences of users openly, we can find out by publicly analyzing historical and real-time data. This is done through a process called matrix factorization. Matrix factorization algorithms work by decomposing the interactive matrix of the user object into a product of two rectangular matrices with a minimum size. This type of recommendation has the added advantage of finding invisible and unmeasured relationships that are not possible with standard content-based filters.

Publisher

Trans Tech Publications Ltd

Reference28 articles.

1. K. Aravindhan S.K.B. Sangeetha,K.Periyakaruppan, Sivani R and Ajithkumar S.(2021).Smart Charging Navigation for VANET based Electric Vehicles" 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021, pp.1588-1591, https://doi.org/10.1109/ICACCS51430.2021.9441842.

2. K.Aravindhan S.K.B. Sangeetha,K.Periyakaruppan K.P. Keerthana, V.Sanjay Giridhar and V.Shyamala Devi,(2021).Design of Attendance Monitoring System using RFID " 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021, pp.1628-1631, https://doi.org/10.1109/ICACCS51430.2021.9441704.

3. R. Chen, Q. Hua, Y.-S. Chang, B. Wang, L. Zhang, and X. Kong, A survey of collaborative filtering-based recommender systems: from traditional methods to hybrid methods based on social networks,, IEEE Access, vol. 6, p.64301–64320, 2018. https://doi.org/10.1109/ACCESS.2018.2877208.

4. L. Cui, P. Ou, X. Fu, Z. Wen, and N. Lu, A novel multi-objective evolutionary algorithm for recommendation systems,, Journal of Parallel and Distributed Computing, vol. 103, p.53–63, 2017. https://doi.org/10.1016/j.jpdc.2016.10.014.

5. Dr.R.Dhaya, S.K.B. Sangeetha, Ashish Sharma, and Jagadeesh.(2017).Improved Performance of Two Server Architecture in Multiple Client Environment, IEEE International Conference on Advanced Computing and Communication Systems, ISBN XPlore No: 978-1-5090-4559-4, Shri Eshwar College of Engineering, Coimbatore, 6th and 7th January. https://doi.org/10.1109/ICACCS.2017.8014560.

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