Shen, X., Jiang, H., Liu, D., Yang, K., Deng, F., Lui, J. C. S., Liu, J., Dustdar, S., & Luo, J. (2022). PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones. IEEE Internet of Things Journal, 9(17), 15538–15553. https://doi.org/10.1109/JIOT.2022.3181607
Energy Optimization; Multilayer Perceptron; Pupillary Response; Random Forest; Recommendation; User Preference Model
-
Abstract:
As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users’ inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users’ inner feelings, we consider using the change of pupil size when browsing to model users’ preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users’ pupil size change information through the front camera of smartphones; it then pre-processes the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user-product matrix and further simplifies it by singular value decomposition. Finally, real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users’ smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users’ preference, and can recommend products users interested in.
en
Project (external):
National Natural Science Foundation of China National Social Science Foundation of China National Natural Science Foundation of China National Natural Science Foundation of China
-
Project ID:
Grant U20A20181 Grant 19ZDA103 Grant 61902060 Grant 61732017