Social Network Based Recommender System to Enhance Quality of Experience (QoE) and Business Intelligence for Service Providers
Keywords:
Recommender system, Intelligent Transportation, Online Social Network (OSN), Ridesharing, Machine LearningAbstract
Ridesharing service is one of the most excellent urban transport models, where two or more customers share a ride. It mainly helps in reducing the transportation cost and count of vehicles moving, thus enhancing user mobility. Ride Sharing presents advantages like reducing traffic and pollution hence promotes Smart City application. In ride sharing application, the substantial new assessment parameter is quality of experience (QoE) by users. This work proposes recommendation system in ride sharing context using quality of experience which utilizes both the user profile details mined from online social network (OSN) and user preferences. The main purpose of the proposed work is to enhance users’ QoE. Based on online social network, the users’ profile is constructed for ridesharing that consists of collection of users with similar personality in the same tour, and eliminates customers with conflicting preferences. Initially, subjective tests are conducted to get users’ preferences information and their outcomes are evaluated using machine learning algorithms to generate user profiles. The experimental results show that Random Forest classifier has shown accuracy of 94%, precision 90% and recall 95%.
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