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Recommending People to Follow Using Asymmetric Factor Models with Social Graphs

  • Tianle Ma
  • Yujiu Yang
  • Liangwei Wang
  • Bo Yuan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)

Abstract

Traditional recommendation techniques often rely on the user-item rating matrix, which explicitly represents a user’s preference among items. Recent studies on recommendations in the scenario of social networks still largely follow this principle. However, the challenge of recommending people to follow in social networks has yet to be studied thoroughly. In this paper, by using the utility instead of ratings and randomly sampling the negative cases in the recommendation log to create a balanced training dataset, we apply the popular matrix factorization techniques to predict whether a user will follow the person recommended or not. The asymmetric factor models are built with an extended item set incorporating the social graph information, which greatly improves the prediction accuracy. Other factors such as sequential patterns, CTR bias, and temporal dynamics are also exploited, which produce promising results on Task 1 of KDD Cup 2012.

Keywords

Social Recommender Systems Matrix Factorization People Recommendation Social Graph Asymmetric Factor Model 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 60905030) and Upgrading Plan Project of Shenzhen Key Laboratory (No. CXB201005250038A). The authors are also grateful to several colleagues who have provided constructive feedbacks to our work. Besides, we would like to gratefully acknowledge the organizers of KDD Cup 2012 as well as Tencent Inc. for making the datasets available.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Intelligent Computing Lab, Graduate School at ShenzhenTsinghua UniversityShenzhenP. R. China
  2. 2.Huawei Noah’s Ark LabHuawei Technologies Co., Ltd.ShenzhenP. R. China

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