Collaborative Co-clustering across Multiple Social Media

Abstract

Combining multiple source information can often lead to improved performance on the learning task. Information from different sources could potentially compensate the missing information in a single source. However, designing an effective combining scheme is not always straightforward in practice. This paper aims to combine information from multiple social media websites to enhance the co-clustering performance of two types of objects (social media objects and users) in one social network, since users could leave footprints across different social media websites, such as Twitter, Foursquare, etc. Data generated from multiple heterogeneous sources can be casted in a multi-view setting. Specifically, we construct the relationship matrix as relationship view and features of each individual object from different sources as different feature views. In previous works, features besides relationship matrix were added to co-clustering in the following manners: different features are taken indiscriminately, those features act as hard constraints to force final co-clusters agree with these constraints. A co-regularized collaborative co-clustering model (Co-CoClust) is proposed to simultaneously perform co-clustering on relationship view and clustering on multiple feature views. In this framework, features from different sources are treated discriminately since they are divided into separate views and utilized based on the distance from relationship matrix. Co-regularization technique is introduced to impose a common constraint between co-clustering and clusterings such that the relationship matrix and feature matrix are unified. By alternating minimization, results of co-clustering and clustering from different views are iteratively optimized. Therefore, co-clustering results are improved by leveraging multiple source information. The proposed algorithm proves its effectiveness in social media datasets and traditional document-word datasets.