A Novel Approach Towards Context Based Recommendations Using Support Vector Machine Methodology

Abstract Majority of recommender systems have their basis on either of the aspectual factors or contextual factors where as very few systems endeavours to demonstrate the use of both factors collectively. Very few works have been done to identify more fine-grained aspect level contextual preferences and their significance in generating accurate predictions for the user. Accuracy has constantly been the centre of all the works performed in improving this system. The purpose of this study is to introduce the use of such a technique that can integrate well into a system that is based on both contextual and non-contextual user preferences. For this purpose, use of a standard machine learning technique, Support Vector Machine was suggested in this paper. SVM facilitates in separating the data via hyperplane, in the finest manner and then classify these data. Users’ preferences are further classified using training set produced as a result of SVM classification. Finally a real-life dataset is experimented to demonstrate that our method is proficient in dealing with contextual as well as non-contextual preferences of users with higher accuracy.