Performance Comparison of Randomized and Non-Randomized Learning Algorithms based Recommender Systems

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Published Oct 31, 2022
https://doi.org/10.47164/ijngc.v13i3.820
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Volume 13, Special Issue 3, October 2022

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Maryam Nadeem
Department of Computer Science and Engineering, SEST, Jamia Hamdard
Mohammed Wasid
Interdisciplinary Centre for Artificial Intelligence, Aligarh Muslim University, Aligarh
Mohammad Nadeem
Department of Computer Science, Aligarh Muslim University, Aligarh
Mohammed Talha Alam
Department of Computer Science and Engineering, SEST, Jamia Hamdard
Shahab Saquib Sohail
Department of Computer Science and Engineering, SEST, Jamia Hamdard
Shakil
Department of Computer Science and Engineering, SEST, Jamia Hamdard
Syed Ubaid
Department of Computer Science and Engineering, SEST, Jamia Hamdard
Sana Shahab
Department of Business Administration, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

Abstract

Recommender System (RS) is an information filtering software tool that provides relevant recommendations to users on various products. Finding the preference of users on products is the core component of RS. Most of the available RS datasets are complex enough to represent the user preferences and usually require a lot of processing before their utilization. On the other hand, selecting an appropriate learning algorithm for making relevant recommendations is a complex task. In this paper, we perform various pre-processing steps on a movie dataset to find the key features that help us to identify the actual user preferences. Moreover, we perform a comparative analysis of various randomized and non-randomized learning algorithms by utilizing these features to identify the best learning algorithm. Our study reinforces the superiority of randomized learning algorithms over non-randomized ones on MovieLens dataset.

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How to Cite
Nadeem, M., Wasid, M., Nadeem, M., Mohammed Talha Alam, Shahab Saquib Sohail, Shakil, Syed Ubaid, & Sana Shahab. (2022). Performance Comparison of Randomized and Non-Randomized Learning Algorithms based Recommender Systems. International Journal of Next-Generation Computing, 13(3). https://doi.org/10.47164/ijngc.v13i3.820
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