Recommender Vision: a recommendation system for commerce based on Computer Vision
Abstract
Recommender systems are useful to improve customer’s buying process by assisting them in decision-making processes. These systems usually use structured data, and unstructured data sets remain poorly explored. In this context, adding computer vision and artificial intelligence techniques can improve the result of the recommendations. This paper presents the progress of a recommender system that suggests products in real-time based on people’s behavior, considering video image analysis. The approach considers the user identity privacy, based on facial correspondence, without citing user identification. The sale suggestion inferences are based on the monitoring of images from a defined region on the store, considering products that users are viewing during a purchase.References
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I. Portugal, P. Alencar, and D. Cowan, "The use of machine learning algorithms in recommender systems: A systematic review," Expert Systems with Applications, pp. 205–227, 2018.
Z. Batmaz, A. Yurekli, A. Bilge, and C. Kaleli, "A review on deep learning for recommender systems: Challenges and remedies," Artificial Intelligence Review, p. 1–37, 6 2018.
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S. Zhang, L. Yao, A. Sun, and Y. Tay, "Deep learning based recommen- der system: A survey and new perspectives," ACM Computing Surveys (CSUR), vol. 52, no. 1, pp. 1–38, 2019.
K. Gidlof, A. Wallin, R. Dewhurst, and K. Holmqvist, "Using ¨ eye tracking to trace a cognitive process: Gaze behaviour during decision making in a natural environment," Journal of Eye Movement Research, vol. 6, no. 1, Apr. 2013. [Online]. Available: https: //bop.unibe.ch/JEMR/article/view/2351
Published
2020-11-07
How to Cite
SEMINOTTI, Malomar Alex; RIEDER, Rafael.
Recommender Vision: a recommendation system for commerce based on Computer Vision. In: WORKSHOP OF WORKS IN PROGRESS - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
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p. 147-150.
DOI: https://doi.org/10.5753/sibgrapi.est.2020.12998.
