User-based recommendations for micro-blogging systems

User-based recommendations for micro-blogging systems

Camelia Constantin Ryadh Dahimene Cédric du Mouza Quentin Grossetti

Univ. Pierre et Marie Curie, 2 Place Jussieu, 75005 Paris, France

CNAM Paris, 2 rue Conté, 75141 Paris, France

Corresponding Author Email: 
camelia.constantin@lip6.fr, dahimene.ryadh@gmail.com,dumouza@cnam.fr, quentin.grossetti@cnam.fr
Page: 
93-118
|
DOI: 
https://doi.org/10.3166/ISI.21.3.93-118
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
30 June 2016
| Citation
Abstract: 

Micro-blogging systems have become a prime source of information. However due to their unprecedented success, these systems have to face an exponentially increasing amount of user generated content. As a consequence, finding users who publish quality content that matches precise interests is a real challenge for the average user. We present in this article a recommendation score which takes advantage of the social graph topology and of the existing contextual information to recommend users to follow on a given topic. We also introduce a landmark-based algorithm that precomputes recommendation scores for a given set of graph nodes and that allows to scale. Our experimental results confirm the relevance of this score against existent approaches as well as the scalability of our landmark-based algorithm.

Keywords: 

recommendation, social networks, micro-blogging

1. Introduction
2. État de l’art
3. Modèle
4. Une estimation efficace des recommandations
5. Expérimentations
6. Conclusion
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