Collaborative online learning of user generated content

Abstract

We study the problem of online classification of user generated content, with the goal of efficiently learning to categorize content generated by individual user. This problem is challenging due to several reasons. First, the huge amount of user generated content demands a highly efficient and scalable classification solution. Second, the categories are typically highly imbalanced, i.e., the number of samples from a particular useful class could be far and few between compared to some others (majority class). In some applications like spam detection, identification of the minority class often has significantly greater value than that of the majority class. Last but not least, when learning a classification model from a group of users, there is a dilemma: A single classification model trained on the entire corpus may fail to capture personalized characteristics such as language and writing styles unique to each user. On the other hand, a personalized model dedicated to each user may be inaccurate due to the scarcity of training data, especially at the very beginning; when users have written just a few articles. To overcome these challenges, we propose learning a global model over all users' data, which is then leveraged to continuously refine the individual models through a collaborative online learning approach. The class imbalance problem is addressed via a cost-sensitive learning approach. Experimental results show that our method is effective and scalable for timely classification of user generated content. © 2011 ACM.