Personalized Recommendation over a Customer Network for Ubiquitous Shopping

Personalization services in a ubiquitous computing environment-ubiquitous personalization services computing-are expected to emerge in diverse environments. Ubiquitous personalization must address limited computational power of personal devices and potential privacy issues. Such characteristics require managing and maintaining a client-side recommendation model for ubiquitous personalization. To implement the client-side recommendation model, this paper proposes Buying-net, a customer network in ubiquitous shopping spaces. Buying-net is operated in a community, called the Buying-net space, of devices, customers, and services that cooperate together to achieve common goals. The customers connect to the Buying-net space using their own devices that contain software performing tasks of learning the customers' preferences, searching for similar customers for network formation, and generating recommendation lists of items. Buying-net attempts to improve recommendation accuracy with less computational time by focusing on local relationship of customers and newly obtained information. We experimented with such customer networks in the area of multimedia content recommendation and validated that Buying-net outperformed a typical collaborative-filtering-based recommender system on accuracy as well as computational time. This shows that Buying-net has good potential to be a system for ubiquitous shopping.