Kiosk Recommend System Based on Self-Supervised Representation Learning of User Behaviors in Offline Retail

Recently, in the offline distribution field, as the number of data collection and analysis cases increases by applying IoT devices to kiosks, research on hyper-personalized recommendation systems has become critical. Recommendation systems only work well in some data-rich areas (industries). Therefore, it is unsuitable for kiosk systems with multiple domains and data imbalances, and it is challenging to collect detailed information, such as user reviews and product descriptions. In this article, we propose a context-aware hyper-personalized recommendation system that utilizes context information collected from kiosk IoT devices, minimizes the model size of the kiosk device, and aims for consistent performance and high-recommendation performance in various domains. We also developed effective self-supervised learning to increase data learning efficiency in data imbalance environments. The quality of products recommended by the proposed kiosk recommendation system was evaluated using transactions that occurred in an actual kiosk system. As a result, compared to the existing recommendation system, all performance indicators improved by an average of 20%. When the self-supervised learning method was additionally applied, it improved by an average of 0.8% more. In particular, it shows superior performance regarding the quality of recommended items and resource usage according to users.