A shopping behavior prediction system: Considering moving patterns and product characteristics

- A shopping behavior prediction (SBP) system is proposed.
- SBP system is designed for mobile commerce environment.
- High-utility mobile sequential patterns are used to model customer behavior.
- Store-to-store (StoS) similarity and item-to-item (ItoI) similarity are considered.

In recent years, the development of location determination technologies such as GPS, Wi-Fi, and RFID have made it possible to collect locational data for moving customers, and many behavior prediction and recommendation systems have been proposed based on moving path and purchase transactions. However, these systems do not take item-specific profit margins into consideration. In addition, few such proposals accounted for location similarity and item similarity. To address these issues, a shopping behavior prediction (SBP) system is proposed consisting of a behavior mining module, a similarity inference module, and a behavior prediction module. The behavior mining module can discover high-utility mobile sequential patterns (UMSPs) using the UMSPL algorithm. In the similarity inference module, store-to-store (StoS) similarities and item-to-item (ItoI) similarities are derived by a proposed similarity inference algorithm. When evaluating StoS and ItoI similarities, the quantities of items purchased are considered. Finally, based on UMSPs, StoS similarities, and ItoI similarities, the behavior prediction module generates a list of shopping suggestions for the target user.