M. E. J. Newman PNAS 2006

Citation

@article{Newman:2006:Proc-Natl-Acad-Sci-U-S-A:16723398,

 author = {Newman, M E},
 journal = {Proc Natl Acad Sci U S A},
 pages = {8577-8582},
 title = {Modularity and community structure in networks},
 volume = 103,
 year = 2006

Online version

Neighborhood Formation and Anomaly Detection in Bipartite Graphs

Summary

This paper points out three aspects which existing social collaborative filtering (social CF) have overlooked; (1) learning similarities among users (2) learning restricted common interests among users and (3) learning direct user-to-user information diffusion. Then this paper proposes a unified framework based on [[UsesMethod::matrix factorization|matrix factorization] that can consider the three aspects by introducing a new objective function.

To evaluate the accuracy of the proposed method, they created a Facebook application that recommends links to users every day and conducted live online user trials over five months, with over 100 Facebook App users and data from over 37,000 Facebook users. In the experiment, they compared the proposed method to several methods (including k-Nearest Neighbor, SVM, Matchbox CF, and several social CF) in live online recommendation tasks, showing that the proposed method has highest accuracy in "like" prediction task. In addition, they conducted user behaviors analyses by using real data they collected. They also got some feedback from users to improve social CF applications.

Strengths and weaknesses

Strength

The problems the authors pointed out regarding existing social CF are genuine to social media, but have not yet been fully considered. The authors propose a method that can solve the problem in a unified way based on MF. In addition, they actually created a Facebook application to collect user behavior data in Facebook. By doing so, they got rich features and conducted detailed analyses on users behaviors, too.

weakness

They manually set the weight for each objective function, and this might be time-consuming in practical situations. In addition, we are not clear which part of extensions actually contributed the increase of accuracy, because there are no systematic analyses. Thus, we cannot get much insight about users behaviors. We also cannot get much insight to improve the proposed method.

Possible impact

If they were able to publish data, it would have much more impact. (Actually, they cannot publish their data because of the requirement from the funding project.) Also, if they conducted analyses about how much each part of the extension contributed to the performance. If they did so, we could have insight about users' behaviors, or ways to improve existing social CF.

Recommendation for whether or not to assign the paper as required/optional reading in later classes.

No. There are not much insight about phenomena in social media.

Related Papers

• There are many papers on how to combine social network and users' other actions.
  • S. H. Yang et al. WWW 2011 : S. H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H. Zha. Like like alike: Joint friendship and interest propagation in social networks.In WWW-11, 2011.

Study Plan

To understand some matrix calculation, I read some of the paper; K. B. Petersen and M. S. Pedersen. The matrix cookbook, 2008.