Applying Deep Learning-Based Personalized Item Recommendation for Mobile Service in Retailor Industry

Abstract

Various kinds of mobile services allow integrating terminal customers as important coproducers into the whole retailer’s business processes. People have enjoyed increasing popularity in the past years since they allow saving costs and increasing satisfaction. However, in some retail settings, as the technology relies on retailers providing terminals, it does not yet fully utilize the possibilities provided by mobile service, which until recently have mostly served as shopping aids. Recommendation systems can provide accurate recommendation services to users, especially in the field of e-commerce. In this study, a mobile retail terminal, Kkbox, leverages deep learning-based recommendation and self-service technologies to provide an express and personalized self-checkout retail environment without the engagement of storekeepers and cashiers. An attention-based mechanism for product personalization recommendation model is adopted, and it models the intrinsic relationship between users’ historical interactions with products through a multilayer self-attentive network and then feeds the output of the multilayer self-attentive network into a GRU network with attention scores to model the evolution of users’ interests. We analyze the performance of the product recommendation module based on user data from multiple perspectives, such as purchase frequency, purchase time, and product category. In the comparison experiments with some traditional recommendation methods, the recommendation accuracy of the model used in this study achieves better results. Besides, it significantly reduces the labor cost and provides enough flexibility. The time performance of app users is independent of store rush. The time for a transaction is significantly lower for app users than the regular shoppers during peak periods. The Kkbox has been deployed in several communities in Taizhou, China, to provide fast and convenient mobile retail services to residents.

Prediction and Replenishment Decision Making for Automatic Pricing of Vegetable Commodities Based on LSTM Models

“…LSTM models can better predict future vegetable demand, thus helping to optimise inventory management and replenishment decisions. Research may include how to combine demand forecasts with actual inventory levels to avoid overstocking or shortages and improve supply chain effectiveness [4]. The application of LSTM models in market trend analysis can provide deeper insights for developing pricing strategies.…”