Gender differentials and implicit feedback on online video content: enhancing user interest evaluation

Abstract

Purpose

Exponential growth in online video content makes viewing choice and video promotion increasingly challenging. While explicit recommendation systems have value, they inherently distract the user from normal behaviour and are open to numerous biases. To enhance user interest evaluation accuracy, the purpose of this paper is to comprehensively examine the relationship between implicit feedback and online video content, and reviews gender differentials in the interest indicated by a comprehensive set of viewer responses.

Design/methodology/approach

This paper includes 200 useable observations based on an experiment of user interaction with the Youku platform (one of the largest video-hosting websites in China). Logistic regression was employed for its simple interpretation to test the proposed hypotheses.

Findings

The findings demonstrate gender differentials in cursor movement behaviour, explainable via well-studied splits in personality, biological factors, primitive behaviour and emotion management. This work offers a solution to the sparsity of work on implicit feedback, contributing to the literature that combines explicit and implicit feedback.

Practical implications

This study offers a launch point for further work on human–computer interaction, and highlights the importance of looking beyond individual metrics to embrace wider human traits in video site design and implementation.

Originality/value

This paper links implicit feedback to online video content for the first time, and demonstrates its value as an interest capturing tool. By reviewing gender differentials in the interest indicated by a comprehensive set of viewer responses, this paper indicates how user characteristics remain critical. Consequently, this work signposts highly fruitful directions for both practitioners and researchers.

Citation

Chong, W., Rudkin, S. and Zhang, J. (2019), "Gender differentials and implicit feedback on online video content: enhancing user interest evaluation", Industrial Management & Data Systems, Vol. 119 No. 5, pp. 1128-1146. https://doi.org/10.1108/IMDS-12-2018-0551