Users’ Reception of Product Recommendations: Analyses Based on Eye Tracking Data
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Journal Volume
12783 LNCS
Pages
90-104
Date Issued
2021
Author(s)
Abstract
Based on eye tracking technology, we study consumers’ overall attention to recommendations appearing at different time settings (i.e., early, mid, and late) and their attention to different information contained in each recommendation, such as recommendation signs, product descriptions, and reviews. By investigating consumers’ eye movement patterns and attention distributions on recommendations, we open the “black box” of why consumers’ reception to recommendations appearing at different time settings varies. The product preference construction literature and mindset theory help to explain why the early recommendations receive the most attention. The need for justification helps to explain why the late recommendations should receive more attention than the mid recommendations. Besides, the fact that not all information appearing in recommendations will receive every customer’s attention inspires a more efficient recommendation page design. By exploring the patterns of consumers’ attention to recommendations, we contribute to the accumulation of recommendation literature and provide guidance for the practice. ? 2021, Springer Nature Switzerland AG.
Subjects
Attention distributions
Eye tracking
Recommendation agents
Eye movements
Black boxes
Eye movement patterns
Eye tracking technologies
Product descriptions
Product recommendation
Provide guidances
Type
conference paper