'Knowing me, knowing you' - using profiles and social networking to improve recommender systems

The Internet has not only brought us more information and choice, but has also increased the burden of making a choice. Recommender systems aim to address this problem by providing personalised recommendations in areas such as music, films or books. Research on recommender systems has focused on improving the matching algorithms. The research presented in this paper takes a user-centred approach. Since recommendations are usually presented as lists of items, without explanations or justifications, users struggle to find out how appropriate recommendations are for them. Our research has shown that the relationship between advice-seeker and recommender is extremely important, so ways of indicating social closeness and taste overlap are required. We thus suggest that drawing on similarity and familiarity between the user and the persons who have rated the items can aid judgement and decision making. This was tested in an experiment, which carefully controlled familiarity, profile similarity and rating-overlap between the user and those rating items. The results help us understand the decision-making processes in an on-line context, and form the basis of a usercentred recommender system approach. We suggest that recommender systems can be improved by combining the benefits of social networking applications - such as explicit networks of trust - with the matching capabilities of recommender systems.