Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kurup, Abhimanyu; Zharon, Antony; Kishore, S.; Manoj, Rakshitha A.; Jayapriya, J.; Vinay, M.; Kokilavani, T. Title A name given to the resource A Comparative Study of Machine Learning Algorithms for Recommendation Systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1276;pp.349-359 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-2697-7_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2697-7_26</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008649859?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008649859?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Decision Tree; Hybrid model; Naive Bayes; Recommendation system; Sparse data Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981962696-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online