A Personalized Device Recommender System in Ubiquitous Environments

Ubiquitous computing enhances computer use by making many computers available throughout the physical environment, while making them effectively invisible to the user. Invisible devices and software are connected to one another to provide convenient services to users. In this environment, users want to get a variety of services by using only an individual mobile device which has limitations such as tiny display screens, limited input, and less powerful processors. In other words, users hope to obtain a personalized service composed of user-suitable devices among sharable devices in a ubiquitous smart space which is called USS in this paper. However, the situations of each user are different and user preferences also are various. Therefore, although users request the same service in the same USS, the most suitable devices for composing the service are different for all users. For satisfying these user requirements, this paper proposes a device recommender system which infers and recommends user-suitable devices for composing a user required service. The objective of this paper is the development of the systems for recommending devices through context-aware inference in peer-to-peer environments. For this goal, this paper considers the environmental context and user preference Also we implement a prototype system and test performance on real ubiquitous mobile object (UMO).