Authors:
Lesly Alejandra Gonzalez Camacho
;
João Henrique Kersul Faria
;
Solange Nice Alves-Souza
and
Lucia Vilela Leite Filgueiras
Affiliation:
Departamento de Engenharia de Computação e Sistemas Digitais, Escola Politécnica da Universidade de São Paulo, São Paulo and Brasil
Keyword(s):
Recommender System, Group Recommender System, Social Network, Social Influence.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
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.
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