Semantic Enabled Recommender System for Micro-Blog Users

Quite a number of recent works have concentrated on the task of recommending to Twitter users whom they should follow, among which, the WTF (Who To Follow) service provided by Twitter. Recommenders are based either on the user's network structure, or on some notion of topical similarity with other users, or on both. We present a method for analysis of Twitter users supported by a hierarchical representation of their interests, which we call a Twixonomy. The use of Twixonomy casts both problems of user classification and recommendation as one of itemset mining, where items are either users' authoritative friends or semantic categories associated to friends. In addition to evaluating our profiler and recommender on several populations, we also show that semantic categories allow for very fine-grained population studies, and make it possible to recommend not only whom to follow, but also topics of interest, users interested in the same topic, and more.