Proactive and reactive context reasoning architecture for smart web services

Sekkal, Nawel; Benslimane, Sidi Mohamed; Mrissa, Michaël; Park, Cheol‐Young; Boudaa, Boudjemaa

doi:10.1504/ijdmmm.2021.10023541

Cited by 1 publication

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“…Reasoning techniques can be classified into (supervised learning, unsupervised learning, reinforcement learning, probabilistic reasoning, and ontology-based reasoning) and evaluated using soundness, efficiency, interoperability, and completeness [19]. In [71], Sekkal et al introduced an approach that combines reactive and proactive reasoning, and they aimed for semantic representation regarding the complex situation with uncertainty.…”

Section: ) Context Reasoningmentioning

confidence: 99%

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Nawara

Kashef

2021

IEEE Access

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An essential goal of recommendation systems is to provide users with accurate and personalized recommendations that meet their preferences. With the rapid growth of IoT-connected sensors, the availability of contextual information has increased, and this has necessitated the fast development of Context-Aware Recommendation Systems (CARS). Context-Aware recommenders are different from traditional recommenders because of their ability to predict the ratings of target users/items by exploiting the knowledge of contextual information. Context-aware recommenders define the context as any information that characterizes the situations of items and users at a particular interaction. They are essential for some contexts where prediction can be more precise in generating specific personalized recommendations. This paper provides a comprehensive review of context-based recommendation systems in IoT environments, namely IoT-CARS, and sheds light on their requirements, characteristics, and applications. We characterize context-aware recommenders in terms of the different IoT contexts and how these contexts are modeled. We also highlight the used metrics to evaluate the performance of various context-based recommenders.

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Section: ) Context Reasoningmentioning

confidence: 99%