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Novel Semantic-Based Probabilistic Context aware Approach for Situations Enrichment and Adaptation

Abstract : This paper aims at ensuring efficient recommendation. It proposes a new context-aware semantic-based probabilistic situations injection and adaptation using ontology approach and Bayesian-classifier. The idea is to predict the relevant situations for recommending the right services. Indeed, situations are correlated with the user's context. It can therefore be considered in designing a recommendation approach to enhance the relevancy by reducing the execution time. The proposed solution in which four probability-based-context rule situation items (user's location and time, user's role, their preferences and experiences) are chosen as inputs to predict user's situations. Subsequently, the weighted linear combination is applied to calculate the similarity of rule items. The higher scores between the selected items are used to identify the relevant user's situations. Three context parameters (CPU speed, sensor availability and RAM size) of the current devices are used to ensure adaptive service recommendation. Experimental results show that the proposed approach enhances accuracy rate with a high number of situations rules. A comparison with existing recommendation approaches shows that the proposed approach is more efficient and decreases the execution time.
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Contributor : Philippe Roose Connect in order to contact the contributor
Submitted on : Friday, September 16, 2022 - 9:45:03 AM
Last modification on : Saturday, September 17, 2022 - 3:44:16 AM


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Abderrahim Lakehal, Adel Alti, Philippe Roose. Novel Semantic-Based Probabilistic Context aware Approach for Situations Enrichment and Adaptation. Applied Sciences, MDPI, 2022, ⟨10.3390/app12020732⟩. ⟨hal-03778704⟩



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