Paper Info
Title
Social Influence-Based Similarity Measures for User-User Collaborative Filtering Applied to Music Recommendation.
Abstract
Social characteristics present in current music streaming services allow to use methods for endowing these systems with more reliable recommendation functionalities. There are many proposals in the literature that take advantage of that information and use it in the context of recommender systems. However, in the specific application domain of music the studies are much more limited, and the methods developed for other domains cannot be often applied since they require social interaction data that are not available in the streaming systems. In this paper, we present a method to determine social influence of users uniquely from friendship relations. The degree of influence obtained is used to define new similarity metrics for collaborative filtering (CF) where more weight is given to more influential users.
Year
DOI
Venue
2018
10.1007/978-3-319-99608-0_30
DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Music recommender systems,Social influence,Trust Collaborative filtering,Streaming services
Recommender system,Social relation,Collaborative filtering,Friendship,Computer science,Social influence,Human–computer interaction,Application domain,Distributed computing
Conference
Volume
ISSN
Citations 
801
2194-5357
0
PageRank 
References 
Authors
0.34
9
5
Name
Order
Citations
PageRank
Diego Sánchez-Moreno1173.41
Javier Pérez-Marcos222.09
Ana B. Gil González3152.37
Vivian F. López Batista46312.89
María N. Moreno García514826.06