ItemConference Paper

A Comparative Study of Machine Learning Algorithms for Recommendation Systems

Title
A Comparative Study of Machine Learning Algorithms for Recommendation Systems
Creator
Kurup, Abhimanyu; Zharon, Antony; Kishore, S.; Manoj, Rakshitha A.; Jayapriya, J.; Vinay, M.; Kokilavani, T.
Description
This research explores recommendation algorithms for e-commerce efficacy. From e-commerce giants like Amazon to streaming services like Netflix, recommendation algorithms are integral in giving personalized experiences to attract and retain customers. It tests KNN, K-Means, Decision Tree (Gini, Entropy), and Naive Bayes on the Amazon review dataset 2018Electronics category. Decision Trees emerged as the most accurate predictor of user preferences, suggesting the trees ability to capture complex data relationships is key for relevant product recommendations. To get a better understanding, this research also examines each algorithms power and weakness in the context of recommendation systems. It offers valuable information on how to approach the optimization of their recommendation strategies in e-commerce businesses, highlighting not only the most effective approach (Decision Trees) but also the considerations for choosing an algorithm based on its strengths and weaknesses (e.g., interpretability vs. accuracy). Ultimately, this research contributes to informing data-driven decision-making for personalized recommendations in e-commerce, paving the way for a more user-centric shopping experience. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1276;pp.349-359
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Decision Tree; Hybrid model; Naive Bayes; Recommendation system; Sparse data
Coverage
Kurup A., CHRIST UNIVERSITY, Karnataka, Bangalore, India; Zharon A., CHRIST UNIVERSITY, Karnataka, Bangalore, India; Kishore S., CHRIST UNIVERSITY, Karnataka, Bangalore, India; Manoj R.A., CHRIST UNIVERSITY, Karnataka, Bangalore, India; Jayapriya J., CHRIST UNIVERSITY, Karnataka, Bangalore, India; Vinay M., CHRIST UNIVERSITY, Karnataka, Bangalore, India; Kokilavani T., CHRIST UNIVERSITY, Karnataka, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981962696-0;
Format
online
Language
English
Type
Conference paper

Citation

Kurup, Abhimanyu; Zharon, Antony; Kishore, S.; Manoj, Rakshitha A.; Jayapriya, J.; Vinay, M.; Kokilavani, T., “A Comparative Study of Machine Learning Algorithms for Recommendation Systems,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25487.

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