Recommendation from robots in a real-world retail shop

By applying network robot technologies, recommendation methods from E-Commerce are incorporated in a retail shop in the real world. We constructed an experimental shop environment where communication robots recommend specific items to the customers according to their purchasing behavior as observed by networked sensors. A recommendation scenario is implemented with three robots and investigated through an experiment. The results indicate that the participants stayed longer in front of the shelves when the communication robots tried to interact with them and were influenced to carry out similar purchasing behaviors as those observed earlier. Other results suggest that the probability of customers' zone transition can be used to anticipate their purchasing behavior.