TY - GEN
T1 - Consumer preferences for the interface of e-commerce product recommendation system
AU - Ku, Yi-Cheng
AU - Peng, Chih-Hung
AU - Yang, Ya-Chi
PY - 2014/6
Y1 - 2014/6
N2 - A recommendation system (RS) in a website is increasingly significant for consumer's decision making. A RS includes several important benefits, such as increasing user satisfaction and building user trust. Despite the growing literature that examined the usefulness of a specific attribute of a RS, less is known about which combination of attributes of a RS is preferable and how the combination influences consumer decision making. By using a conjoint analysis, we can further explore the impacts of combination attributes. In a lab experiment, we find that the importance ranking of attributes of a RS for the participants is quite different. Specifically, all the participants consider the attribute, "Explanation for Recommendation", is important. In addition, "Rating" is important for the specific participants. Furthermore, "Comment" seems to be less important to all the participants. Our results have important implications for the design of a RS. © 2014 Springer International Publishing.
AB - A recommendation system (RS) in a website is increasingly significant for consumer's decision making. A RS includes several important benefits, such as increasing user satisfaction and building user trust. Despite the growing literature that examined the usefulness of a specific attribute of a RS, less is known about which combination of attributes of a RS is preferable and how the combination influences consumer decision making. By using a conjoint analysis, we can further explore the impacts of combination attributes. In a lab experiment, we find that the importance ranking of attributes of a RS for the participants is quite different. Specifically, all the participants consider the attribute, "Explanation for Recommendation", is important. In addition, "Rating" is important for the specific participants. Furthermore, "Comment" seems to be less important to all the participants. Our results have important implications for the design of a RS. © 2014 Springer International Publishing.
KW - adaptive interface
KW - conjoint analysis
KW - recommendation system
KW - user interface preferences
UR - https://www.scopus.com/pages/publications/84903727215
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84903727215&origin=recordpage
U2 - 10.1007/978-3-319-07293-7_51
DO - 10.1007/978-3-319-07293-7_51
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783319072920
VL - 8527 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 526
EP - 537
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 1st International Conference on HCI in Business (HCIB 2014) - Held as Part of 16th International Conference on Human-Computer Interaction (HCI International 2014)
Y2 - 22 June 2014 through 27 June 2014
ER -