Towards a Residential Micro-Location Based Product and Service Recommender System

Context aware product recommender systems are an ongoing field of [1]. Contextual information in this case can for example be the customer’s mood, her age, or place of interaction between the recommender system and the user. The well-established pervasiveness of smartphones even allows to extend a customer's context by location (e.g. [2]) which is easily derived using GPS or WiFi. As soon as the location of a person is known it is simple to recommend suitable products. In the retail industry it is of particular interest to know where exactly in the store a customer is located to send out personalized coupons or offers related to the specific department. While in theory very interesting, it has been shown that the technology available is either inconvenient (e.g., NFC tags placed on shelf) or unreliable (e.g., Wi-Fi triangulation). Lately Apple introduced iBeacon [3] which overcomes some weaknesses of GPS and Wi-Fi and theoretically allows in-store navigation. That way a recommender system would be able to recommend truly context aware recommendations using micro-locating services, e.g., promoting current wine discounts only to customers standing in the wine department. It is reasonable that all those technologies try to provide recommendations near the actual product to provoke immediate purchase decision. Various mobile shopping apps exist, allowing customers to purchase products or services wherever they are and independent of a physical point-of-sale in their proximity. Thus, as smartphones are ubiquitous the point of sale became ubiquitous as well (cf. Amazon or Ebay). Taking this into account we are currently developing a micro-location based recommender system that is aware of the current location of the customer in her own apartment. This allows to recommend suitable products there similar to in-store promotions based on the actual place of interaction between the recommender system and the user.