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Article
Publication date: 25 January 2022

Saeid SadighZadeh and Marjan Kaedi

Online businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their…

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Abstract

Purpose

Online businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their interests explicitly. The purpose of this study is to predict users’ implicit interest in products of an online store based on their mouse behavior through various product page elements.

Design/methodology/approach

First, user mouse behavior data is collected throughout an online store website. Next, several mouse behavioral features on the product pages elements are extracted and finally, several models are extracted using machine learning techniques to predict a user’s interest in a product.

Findings

The results indicate that focusing on mouse behavior on various page elements improves user preference prediction accuracy compared to other available methods.

Research limitations/implications

User mouse behavior was used to predict consumer preferences in this study, therefore gathering additional data on user demography, personality dimensions and emotions may significantly aid in accurate prediction.

Originality/value

Mouse behavior is the most repeated behavior during Web page browsing through personal computers and laptops. It has been referred to as implicit feedback in some studies and an effective way to ascertain user preference. In these studies, mouse behavior is only assessed throughout the entire Web page, lacking a focus on different page elements. It is assumed that in online stores, user interaction with key elements of a product page, such as an image gallery, user reviews, a description and features and specifications, can be highly informative and aid in determining the user’s interest in that product.

Details

Journal of Systems and Information Technology, vol. 24 no. 2
Type: Research Article
ISSN: 1328-7265

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