Recommendation in Offline Stores: A Gamification Approach for Learning the Spatiotemporal Representation of Indoor Shopping
Pages 3878 - 3888
Abstract
With the current advancements in mobile and sensing technologies used to collect real-time data in offline stores, retailers and wholesalers have attempted to develop recommender systems to enhance sales and customer experience. However, existing studies on recommender systems have primarily focused on e-commerce platforms and other online services. They did not consider the unique features of indoor shopping in real stores such as the physical environments and objects, which significantly affect the movement and purchase behaviors of customers, thereby representing the "spatiotemporal contexts" that are critical to identifying recommendable items. In this study, we propose a gamification approach wherein a real store is emulated in a pixel world and a recurrent convolutional network is trained to learn the spatiotemporal representation of offline shopping. The superiority and advantages of our method over existing sequential recommender systems are demonstrated through a real-world application in a hypermarket. We believe that our work can significantly contribute to promoting the practice of providing recommendations in offline stores and services.
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Index Terms
- Recommendation in Offline Stores: A Gamification Approach for Learning the Spatiotemporal Representation of Indoor Shopping
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August 2022
5033 pages
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Published: 14 August 2022
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- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- Institute of Information & Communications Technology Planning & Evaluation, South Korea
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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 14 - 18, 2022
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