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[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.
BibTeX
| @inproceedings { | |||
| GdT17a, | |||
| title | = | "{Enhance micro-blogging recommendations of posts with an homophily-based graph}", | |
| author | = | " Q. Grossetti and C. du Mouza and N. Travers ", | |
| booktitle | = | "{BDA'17}", | |
| year | = | 2017, | |
| month | = | "November", | |
| pages | = | "1--10", | |
| address | = | "Nancy, France", | |
| } | |||
