1. Community-based User Recommendation in Uni-Directional Social Networks
Gang Zhao, Mong Li Lee, Wynne Hsu, Wei Chen, Haoji Hu
School of Computing, National University of Singapore

2. Contents

3. Purpose
Design an user recommendation system in Twitter-style social network
Find a set of users whom a target user is likely to follow

4. Challenges Tweet comments are typically short and noisy
Data is very sparse

5. Proposed Solution Forming communities to reduce data sparsity
Applying matrix factorization on each communities

6. Twitter-style Social Network

7. Discover Communities Framework (1)
U is the set of all users
F is the set of followers
G is the set of followees
𝑑 𝑓 is the list of followees of user u
𝑑 𝑔 is the list of followers of user u

8. Discover Communities Framework (2)
Choose number of topics
Apply Latent Dirichlet Allocation (LDA) to determine the topic distribution of users
For each topic z, form a community c:

9. Recommend Followees (1)
For each community c, construct matrix M with size |c.F| x |c.G|
Apply Implicit Feedback-Matrix Factorization (IF-MF)
Obtained matrix and

10. Recommend Followees (2)
Row vectors associate with followers
Column vectors associate with followees

11. Datasets

12. Evaluation Metrics

13. Experiments (1)

14. Experiments (2)

15. Q & A