Diffusion-aware personalized social update recommendation

  • Authors:

  • Ye Pan;Feng Cong;Kailong Chen;Yong Yu

  • Affiliations:

  • Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai, China;Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • Proceedings of the 7th ACM conference on Recommender systems
  • Year:
  • 2013

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Abstract

Many Internet users have encountered serious information overload problem on social networks such as Facebook and Twitter, where users can consume the streams of social updates from their social connections. Traditional methods solving this problem include collaborative filtering and information diffusion modeling. Both methods answer the "who will adopt what" question from different perspective, while either of them only captures single-faceted knowledge of evidences. In this paper, we solve the personalized social update recommendation problem by proposing a framework which integrates the advantages of collaborative filtering and the characteristics of diffusion processes. The main contributions of this paper are three folds. First, we propose a plenty of diffusion features which capture the characteristics of diffusion processes. Second, we build a joint model which takes the advantages of both collaborative filtering and the characteristics of diffusion processes for recommendation. Finally, experiments on two real-world datasets show that our joint model outperforms the methods capturing single-faceted knowledge and several other baselines.