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

Contents

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

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

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

Twitter-style Social Network

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

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:

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

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

Datasets

Evaluation Metrics

Experiments (1)

Experiments (2)

Q & A