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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.

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