Hybrid location-centric e-Commerce recommendation model using dynamic behavioral traits of customer

Abstract

Major e-Commerce service provider offers additional product recommendation to its customers, while they access the application, and enough evidence existing that such recommendations are cost effective for both consumer and service provider. For maximizing profit and to satisfy the user, existing e-Commerce platforms use long-term context for recommendations. In actual scenario, the recommendation can aid the user for other reason such as when the product is reminded of recent interest in or, point customer to currently discounted items. Furthermore, user preference changes over time due to weather, location, etc. As a result, the recommendation must be made based on the present behavior of the ongoing session. Many research based on location and session-based approaches has been presented to forecast user’s next-item requirement. However, these models are not efficient, as they are designed either to model short-term or long-term preferences. Recently, some hybrid recommendation algorithms have been presented to model both short-term and long term, but these models are designed considering static behavior and finds difficulty in revealing the correlations among behaviors and items. Furthermore, these models do not consider location-centric information for performing recommendation. To overcome the above-mentioned challenges, our research work presents hybrid location-centric prediction (HLCP) model by considering the dynamic behavior traits of users. HLCP model can learn both short-term and long-term context efficiently. Experiment results show that HLCP attains significant performance over existing models in terms of mean reciprocal rate (MMR) and hit rate (HR).