[GdT17a] Enhance micro-blogging recommendations of posts with an homophily-based graph

Conférence Nationale avec comité de lecture : BDA'17, November 2017, pp.1--10, Nancy, France,

Mots clés: Twitter, Recommandation, homophily, SimGraph

Résumé: Due to the popularity of microblogging platforms, the amount of data procuded by users are unprecedent. One major issue is to nd relevant information for each end-users, especially on real-time delivery. Faced with such a volumetry, posts with short lifetime, variety of behaviors between users and content, it becomes a real challenge for recommending systems. Traditional methods like collaborative filtering wil hardly scale up due to the high dynamicity. We present in this article a thorough study of a large Twitter dataset, focused on homophily, which leads to our recommandation approach. It relies on the construction of a similarity graph based on retweet behaviors on top of the Twi er graph. Finally we conduct experiments on our real dataset to demonstrate the quality and scalability of our method.


@inproceedings {
title="{Enhance micro-blogging recommendations of posts with an homophily-based graph}",
author=" Q. Grossetti and C. du Mouza and N. Travers ",
address="Nancy, France",