Empirical analysis of Machine Learning Techniques for context aware Recommender Systems in the environment of IoT

Abstract

At present recommender systems (RS) are incorporating contextual and social data of the client, delivering context aware RS where it take request response approach in which the recommendations are given to the client upon his solicitation. Later on they will utilize verifiable data of the client anywhere from the Internet of Things (IoT). Currently, a proactive RS that pushes proposals to the client when the present circumstance appears proper, without unequivocal client demand has been presented in the exploration research field of RS. In this paper, an outline of a context aware RS that prescribes recommendations under the IoT worldview is proposed. We also did the empirical analysis of the Machine Learning (ML) techniques like Genetic Algorithm optimized Artificial Neural Network (GA-ANN), decision tree, ANN, bagging, boosting which will perform the reasoning of the context. All inputs are virtually derived from the IoT and its output scores are calculated based on ML techniques which will decide if to push a recommendation to the user or not. Benchmark data of the Chicago restaurant is analyzed and it was observed that for the 98% of the contexts, GA-ANN produced correct recommendations with higher accuracy and efficiency in the correct times and context.