Recommendation of items using a social-based collaborative filtering approach and classification techniques

With the large amount of data generated every day in social networks, the use of classification techniques becomes a necessity. The clustering-based approaches reduce the search space by clustering similar users or items together. We focus in this paper on the personalised item recommendation in social context. Our approach combines in different ways the social filtering algorithm and the traditional user-based collaborative filtering algorithm. The social information is formalised by some social-behaviour metrics such as friendship, commitment and trust degrees of users. Moreover, two classification techniques are used: an unsupervised technique applied initially to all users and a supervised technique applied to newly added users. Finally, the proposed approach has been experimented using different existing datasets. The obtained results show the contribution of integrating social information on the collaborative filtering and the added value of using the classification techniques on the different algorithms in terms of the recommendation accuracy.