Automated Product Localization through Mobile Data Analysis

Recent developments in the field of indoor RealTime Locating Systems (RTLS) using mobile devices stimulate decision support for users. For instance, smartphone-based navigation in shops can enable location-aware recommendations of certain products to customers. An impeding factor to realize such systems is that they need the exact position of products. Existing product localization solutions, however, are based on tagging or manual location registering which tend to be quite costly and laborious. In this paper, we propose an automated product localization approach solving this problem. Our system infers the location of products based on the results of accumulating two sets of customer data, i.e., the locations at which the customers stop for picking up items as well as the list of the items, they purchase. These two data sets are accumulated for a large number of users, making it possible to build correct mappings between the products and their positions. We introduce a basic version of our localization algorithm and two extensions. One helps to improve calculating the position of relocated products while the other one fosters a faster localization using a smaller number of user data sets. We discuss the results of various simulation runs which give evidence that our system has a good potential to work in practice.