A CONTEXT-AWARE RECOMMENDER SYSTEM USING THE SENTIMENT ANALYSIS OF TWEETS IN THE ENVIRONMENT OF INTERNET OF THINGS

Abstract

Currently recommender systems are incorporating context and social information of the user, producing context aware recommender systems. In the future, they will use implicit, local and personal information of the user from the Internet of Things; where anyone and anything will be connected at anytime and anywhere. A Context- Aware Recommendation System has been introduced in this thesis. The fact that the future is for Internet of Things, and the multiple recommendation leads to my system design, in which multi-type rather than one type of recommendations will be recommended to the user. In this paper, a design of a context aware recommender system that recommends different types of items under the Internet of Things paradigm is proposed. A major part of this design is the context aware management system. In this system, we have used a neural network that will do the reasoning of the context to determine whether to push a recommendation or not and what type of items to recommend. The neural network inputs are derived virtually from the Internet of Things, and its outputs are scores for three types of recommendations, they are: songs, movies and none. These scores have been used to decide whether to push a recommendation or not, and what type of recommendations to. The results of 1000 random contexts were tested. For an average of 98.80% of them, our trained neural network generated correct recommendation types in the correct times and contexts.