On analyzing user preference dynamics with temporal social networks

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Pereira, FSF (Author) Other; João Gama (Author) FEP; de Amo, S (Author) Other; Oliveira, GMB (Author) Other

Journal

Title: Machine Learning

Vol. 107

Pages: 1745-1773

ISSN: 0885-6125

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 24 Citations

Scopus - 31 Citations

Other information

Authenticus ID: P-00P-3AT

DOI: 10.1007/s10994-018-5740-2

Abstract (EN): The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks in information retrieval and recommendation systems domains. However, existing models are too constrained for capturing the complexity of the underlying phenomenon. Online social networks contain rich information about social interactions and relations. Thus, these become an essential source of knowledge for the understanding of user preferences evolution. In this work, we investigate the interplay between user preferences and social networks over time. First, we propose a temporal preference model able to detect preference change events of a given user. Following this, we use temporal networks concepts to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Finally, we look for a correlation between preference change events and node centrality change events over Twitter and Jam social music datasets. Our findings show that there is a strong correlation between both change events, specially when modeling social interactions by means of a temporal network.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 29