Users’ Reception of Product Recommendations: Analyses based on Eye Tracking Data
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.
Jia, F., Shi, Y., Sia, C. L., Tan, C. H., Nah, F. F., & Siau, K. (2021). Users’ Reception of Product Recommendations: Analyses based on Eye Tracking Data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12783 LNCS, pp. 90-104. Springer Verlag.
The definitive version is available at https://doi.org/10.1007/978-3-030-77750-0_6
International Conference on Human-Computer Interaction, HCII 2021 (2021: Jul. 24-29, Virtual)
Business and Information Technology
Keywords and Phrases
Attention distributions; Eye tracking; Recommendation agents
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2021 Springer Verlag, All rights reserved.
29 Jul 2021
This research is partially supported by the Hong Kong Research Grants Council and City University of Hong Kong (Project No. CityU 11504417/11507619/9360147), a Taiwan Yushan Scholar grant NTU-109V0701, and the National Natural Science Foundation of China (NSFC No. 71701043).