Analysing the Effect of Platform and Operating System Features on Predicting Consumers’ Purchase Intent using Machine Learning Algorithms

Ramazan Esmeli; Alaa Mohasseb; Mohamed Bader-El-Den

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Paper

Analysing the Effect of Platform and Operating System Features on Predicting Consumers’ Purchase Intent using Machine Learning Algorithms

Topics: Context Discovery; Data Mining in Electronic Commerce; Feature Selection; Machine Learning; Pre-Processing and Post-Processing for Data Mining

In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 0IC3K, 333-340, 2020

Authors: Ramazan Esmeli ; Alaa Mohasseb and Mohamed Bader-El-Den

Affiliation: School of Computing, University of Portsmouth, Portsmouth, U.K.

Keyword(s): Purchase Intention Prediction, Purchase Behaviour Prediction, Browsing Behaviour, Classification, Machine Learning.

Abstract: Predicting future consumer browsing and purchase behaviour has become crucial to many marketing platforms. Consumer purchase intention is one of the main inputs used as a measurement for consumer demand for new products. In addition, identifying consumers’ purchase intent play an important role in recommender systems. In this paper, the effect of using different platforms on users’ behaviours is explored. In addition, the effect of users’ platforms and their purchase intentions behaviours are investigated. We conduct computational experiments using different machine learning algorithms in order to investigate the using users’ operating system and platform types as features. The results showed that the users’ purchase intentions and behaviours are correlated with these features.

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Paper citation in several formats:

Esmeli, R.; Mohasseb, A. and Bader-El-Den, M. (2020). Analysing the Effect of Platform and Operating System Features on Predicting Consumers’ Purchase Intent using Machine Learning Algorithms. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KDIR; ISBN 978-989-758-474-9; ISSN 2184-3228, SciTePress, pages 333-340. DOI: 10.5220/0010176803330340

@conference{kdir20,
author={Ramazan Esmeli. and Alaa Mohasseb. and Mohamed Bader{-}El{-}Den.},
title={Analysing the Effect of Platform and Operating System Features on Predicting Consumers’ Purchase Intent using Machine Learning Algorithms},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KDIR},
year={2020},
pages={333-340},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010176803330340},
isbn={978-989-758-474-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KDIR
TI - Analysing the Effect of Platform and Operating System Features on Predicting Consumers’ Purchase Intent using Machine Learning Algorithms
SN - 978-989-758-474-9
IS - 2184-3228
AU - Esmeli, R.
AU - Mohasseb, A.
AU - Bader-El-Den, M.
PY - 2020
SP - 333
EP - 340
DO - 10.5220/0010176803330340
PB - SciTePress