Social Tracks: Recommender System for Multiple Individuals using Social Influence

Lesly Camacho, João Faria, Solange Alves-Souza, Lucia Filgueiras


The number of data generated through interactions within a social network, or interactions within a platform resources (eg. clicks, hits, purchases), grow exponentially over time. The popularization of social networks and the increase of interactions allow data to be analyzed to predict the tastes and desires of consumers. The use of recommendation systems to filter content based on the characteristics and tastes of a user is already widespread and applied across platforms. However, the application of recommendation systems to multiple individuals is a less explored field. For this project, data was gathered from social networks to recommend music playlists to a group of individuals. Listening to music as a group is a common activity, be it with friends, couples or in parties. Social network data are used to identify the social influence of the individuals in the group. In addition, to identify the preferences, the characteristics of the songs most frequently heard by the members of the group are assembled. Matrix factorization is used to predict group interests. Proposed influence factor, based on similarity, leadership and expertise, is added to compute a final recommendation. A social network was created to support the controlled experiment, the results show the prediction made by the system vary of 1,455 of the ratings made by the group' members.


Paper Citation