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Impact of Personalization Algorithms on Beverage Selling Website

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Impact of Personalization Algorithms on Beverage Selling Website


Devashish Sonewane | Om Chopkar | Sumit Yadav | Tejas Burade | Prof. Rutika Gahlod



Devashish Sonewane | Om Chopkar | Sumit Yadav | Tejas Burade | Prof. Rutika Gahlod "Impact of Personalization Algorithms on Beverage Selling Website" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-5, October 2024, pp.607-615, URL: https://www.ijtsrd.com/papers/ijtsrd69423.pdf

The impact of personalization algorithms on beverage selling websites is a crucial aspect of e-commerce, as it can significantly increase engagement and sales. Personalization and recommendation algorithms play a vital role in enhancing the online shopping experience, making it more tailored to individual customers' preferences. This topic explores the effects of personalization algorithms on online beverage stores, focusing on collaborative filtering and content-based recommendations. Collaborative filtering involves gathering individuals with similar interests or characteristics and providing their feedback to users in the same cluster for reference. This approach satisfies customers' mentality of referring to others' opinions before making decisions. On the other hand, content-based recommendations suggest similar items or content that the user has previously searched for, viewed, purchased, or rated positively. The use of learning techniques in recommendation systems can improve the accuracy and scalability of these algorithms. By enhancing personalization algorithms, online beverage stores can increase customer satisfaction, loyalty, and sales. This topic aims to investigate the impact of personalization algorithms on beverage selling websites and explore the potential of collaborative filtering and content-based recommendations in enhancing the online shopping experience.

Personalization algorithms, E-commerce engagement, Recommendation systems, Collaborative filtering, Content-based recommendations, Customer satisfaction, Sales optimization, Algorithm learning techniques


IJTSRD69423
Volume-8 | Issue-5, October 2024
607-615
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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