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In the last years, we have witnessed the introduction of the Internet of Things (IoT) as an integral part of the Internet with billions of interconnected and addressable everyday objects. On one hand, these objects generate a massive volume of data that can be exploited to gain useful insights into our day-to-day needs. On the other hand, context-aware recommender systems (CARSs) are intelligent systems that assist users to make service consumption choices that satisfy their preferences based on their contextual situations. However, one of the key challenges facing the development and deployment of CARSs is the lack of functionality for providing dynamic and reliable context information required by the recommendation decision process. Thus, data obtained from IoT objects and other sources can be exploited to build CARSs that satisfy users’ preferences, improve quality of experience, and boost recommendation accuracy. This article describes various components of a conceptual IoT-based framework for context-aware personalized recommendations. The framework addresses the weakness whereby CARSs rely on static and limited contexts from user’s mobile phone by providing additional components for reliable and dynamic context information, using IoT context sources. The core of the framework consists of a context classification and reasoning management and a dynamic user profile model, incorporating trust to improve the accuracy of context-aware personalized recommendations. Experimental evaluations show that incorporating context and trust into personalized recommendation process can improve accuracy.

A Framework for Exploiting Internet of Things for Context-Aware Trust-Based Personalized Services