Hybrid Social Recommender Systems for Electronic Commerce: A Review

Electronic commerce, widely known as e-commerce, has been a very promising sector for buying and selling products over the Internet. Primarily, this is of wide-ranging importance due to the huge involvement of all sorts of transactions. Use of recommendation systems (RSs) in aid of e-commerce will not only increase the profit, but also render in the conversion of browsers to buyers and enhance the loyalty of a user. In this paper, we discuss various hybrid social RSs that make use of several social factors. In addition, we use Thomas-Kilmann conflict mode instrument (TKI) test and analytic hierarchy process (AHP) to show their efficiency in the RSs. There is always a quest to involve maximum social information to enhance the recommendations about a given product. Therefore, this paper inculcates maximum social factors, namely the distance between the individuals, similarity in the recommendations made, trust and relationships to improve the accuracy of the recommendations. We discuss a proposal on a hybrid social RS, which uses TKI test and AHP. It is observed that there is a huge involvement of intimacy and intensity with respect to trust and relationship in order to make the recommendations.