Principles to Design Smart Physical Objects as Adaptive Recommenders
Federica Cena, Luca Console, Assunta Matassa, Ilaria Torre · IEEE Access · 2017
Recommenders have proven to be useful means to support people in their activities and in making decisions. They evolved from online recommenders to context-aware and ubiquitous recommenders. Moving forward along this line, this paper introduces the new emerging class of smart physical recommenders: context-aware recommender systems that are embedded into physical everyday objects. This paper describes the features of these systems and presents a conceptual model to design them, by analyzing a number of issues that have to be addressed by a designer and discussing the consequences of different design choices with their impact on the smartness of the designed object. The model is structured in a number of layers corresponding to different conceptual design phases in which different requirements are analyzed. The contribution of this paper is to discuss and provide design guidelines for a new rising class of recommenders that combine the features of intelligent agents, cyber-physical objects, and recommender-support systems. The description of the model is complemented by an exemplary analysis of its application.