An Homophily-based Approach for Fast Post Recommendation in Microblogging Systems

Abstract : With the unprecedented growth of user-generated content produced on microblogging platforms, finding interesting content for a given user has become a major issue. However due to the intrinsic properties of microblogging systems, such as the vol-umetry, the short lifetime of posts and the sparsity of interactions between users and content, recommender systems cannot rely on traditional methods, such as collaborative filtering matrix factorization. After a thorough study of a large Twitter dataset, we present a propagation model which relies on homophily to propose post recommendations. Our approach relies on the construction of a similarity graph based on retweet behaviors on top of the Twitter graph. Finally we conduct experiments on our real dataset to demonstrate the quality and scalability of our method.
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Quentin Grossetti, Camelia Constantin, Cédric Du Mouza, Nicolas Travers. An Homophily-based Approach for Fast Post Recommendation in Microblogging Systems. 21st International Conference on Extending Database Technology (EDBT 2018), Mar 2018, Vienne, Austria. pp.229-240, ⟨10.5441/002/edbt.2018.21⟩. ⟨hal-01679120⟩



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