Cross-OSN User Modeling by Homogeneous Behavior Quantification and Local Social Regularization
In the context of social media services, data shortage has severally hindered accurate user modeling and practical personalized applications. This paper is motivated to leverage the user data distributed in disparate online social networks (OSN) to make up for the data shortage in user modeling, which we refer to as “cross-OSN user modeling.” Generally, the data that the same user distributes in different OSNs consist of both behavior data (i.e., interaction with multimedia items) and social data (i.e., interaction between users). This paper focuses on the following two challenges: 1) how to aggregate the users' cross-OSN interactions with multimedia items of the same modality, which we call cross-OSN homogeneous behaviors, and 2) how to integrate users' cross-OSN social data with behavior data. Our proposed solution to address the challenges consist of two corresponding components as follows. 1) Homogeneous behavior quantification, where homogeneous user behaviors are quantified based on their importance in reflecting user preferences. After quantification, the examined cross-OSN user behaviors are aggregated to construct a unified user-item interaction matrix. 2) Local social regularization, where the cross-OSN social data is integrated as regularization in matrix factorization-based user modeling at local topic level. The proposed cross-OSN user modeling solution is evaluated in the application of personalized video recommendation. Carefully designed experiments on self-collected Google+ and YouTube datasets have validated its effectiveness and the advantage over single-OSN-based methods.