Social Tracks: Recommender System for Multiple Individuals using Social Influence

Lesly Alejandra Gonzalez Camacho, João Henrique Kersul Faria, Solange Nice Alves-Souza, Lucia Vilela Leite Filgueiras

2019

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.

Paper Citation

in Harvard Style

Camacho L., Faria J., Alves-Souza S. and Filgueiras L. (2019). Social Tracks: Recommender System for Multiple Individuals using Social Influence. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 363-371. DOI: 10.5220/0008166503630371

in Bibtex Style

@conference{kdir19,
author={Lesly Alejandra Gonzalez Camacho and João Henrique Kersul Faria and Solange Nice Alves-Souza and Lucia Vilela Leite Filgueiras},
title={Social Tracks: Recommender System for Multiple Individuals using Social Influence},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={363-371},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008166503630371},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Social Tracks: Recommender System for Multiple Individuals using Social Influence
SN - 978-989-758-382-7
AU - Camacho L.
AU - Faria J.
AU - Alves-Souza S.
AU - Filgueiras L.
PY - 2019
SP - 363
EP - 371
DO - 10.5220/0008166503630371
PB - SciTePress