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Usage Data for Predicting User Trends and Behavioral Analysis in E-Commerce Applications

Usage Data for Predicting User Trends and Behavioral Analysis in E-Commerce Applications

Sathiyamoorthi V., T. Nadana Ravishankar, Ilavarasi A. K., Sridhar Udayakumar, Karthikeyan Harimoorthy, Jayapandian N., Saravanan V.
Copyright: © 2021 |Volume: 13 |Issue: 4 |Pages: 22
ISSN: 1935-5688|EISSN: 1935-5696|EISBN13: 9781799859994|DOI: 10.4018/IJISSS.2021100103
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MLA

Sathiyamoorthi V., et al. "Usage Data for Predicting User Trends and Behavioral Analysis in E-Commerce Applications." IJISSS vol.13, no.4 2021: pp.40-61. http://doi.org/10.4018/IJISSS.2021100103

APA

Sathiyamoorthi V., Ravishankar, T. N., Ilavarasi A. K., Udayakumar, S., Harimoorthy, K., Jayapandian N., & Saravanan V. (2021). Usage Data for Predicting User Trends and Behavioral Analysis in E-Commerce Applications. International Journal of Information Systems in the Service Sector (IJISSS), 13(4), 40-61. http://doi.org/10.4018/IJISSS.2021100103

Chicago

Sathiyamoorthi V., et al. "Usage Data for Predicting User Trends and Behavioral Analysis in E-Commerce Applications," International Journal of Information Systems in the Service Sector (IJISSS) 13, no.4: 40-61. http://doi.org/10.4018/IJISSS.2021100103

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Abstract

Reviewing and buying the right goods from online websites is growing day by day in today's fast internet environment. Numerous goods in the same label are available to consumers. It is thus a difficult job for consumers to pick up the correct commodity at a decent price under different market conditions. Therefore, it is important for owners of online shopping websites to better understand their customers' needs and offer better services. For these reasons, the access log documented a vast amount of data related to user interactions with the websites. This access log therefore plays a key role in predicting user access trends and in recommending the best product to consumers. This research work therefore focuses on one such methodology for evaluating the pattern and behavioral analysis of users in e-commerce websites.

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