Machine Learning Algorithm to Improve User's Experience

Abstract

Need: The previous suggestion assists with administrative methodology. The contribution explores customer understandings in different industry and transaction texts. They include online education, video marketing, and entertainment analytics. The communication needs to be detailed to improve the system.

Purpose: The suggestion aims to improve the previous contribution by enhancing the user experience. The study increases the usage of video content. The recommendation brings better business to the video host.

Methodology: The work includes the machine learning algorithm to understand the user and improve the client’s experience. The recommendation uses the Apriori algorithm to map various attributes of the trainer and learners. The suggested work has three features. It focusses on video possessions, educator feelings, physical characteristics, and visible aesthetic characteristics. The study considers 1,200 different samples.

Findings: The work simulates using python. It improves efficiency by 29.5% compared to previous work.

Practical Implications: Machine learning has pitched in to understand diverse customers’ behaviour. Various features affecting the behaviour are collected and analysed by the system. The study intends to find an appropriate mapping between the attributes of the user and educator.

Citation

Ambika, N. (2023), "Machine Learning Algorithm to Improve User's Experience", Grima, S., Sood, K. and Özen, E. (Ed.) Contemporary Studies of Risks in Emerging Technology, Part A (Emerald Studies in Finance, Insurance, and Risk Management), Emerald Publishing Limited, Leeds, pp. 49-59. https://doi.org/10.1108/978-1-80455-562-020231004