1. Personalized Video Recommendation Based on Cross-Platform User Modeling
Zhengyu Deng, Jitao Sang, Changsheng Xu
1 Institute of Automation, Chinese Academy of Sciences
2 Chinese-Singapore Institute of Digital Media

2. Summary Cross-platform user modeling
The limitations of single platform
Cold-start issue
Data sparsity
homogeneous
Cross-platform user modeling
A user has multiple accounts across web2.0, which reflect user interest from different perspectives
We can utilize one platform to enrich another or generate a unified user profile by aggregating the distributed user info across different platform.

3. Framework
--We use YouTube as the target platform where to perform the recommendation task, and Google+ as the auxiliary platform where user information is transferred.
--Two strategies are designed to strengthen the understanding of user interest in the target platform: one is profile enrichment and the other is collaborative relationship transfer.

4. Approach Kernel-based similarity learning
--We assume that users who have similar profiles in Google+ are very likely to have similar profiles in YouTube.
--We measure user similarity under different modalities using multiple kernel learning (MKL) scheme to integrate the multiple modalities

5. Experiments Quantitative results
Recommend only by YouTube Profile (S1);
Recommend by Profile Enrichment (S2);
Recommend by YouTube Profile with Collaborative Transfer (S3);
Recommend by Profile Enrichment with Collaborative Transfer (S4).