Content + Attributes: A Latent Factor Model for Recommending Scientific Papers in Heterogeneous Academic Networks

  • Chenyi Zhang
  • Xueyi Zhao
  • Ke Wang
  • Jianling Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)


This paper focuses on the precise recommendation of scientific papers in academic networks where users’ social structure, items’ content and attributes exist and have to be profoundly exploited. Different from conventional collaborative filtering cases with only a user-item utility matrix, we study the standard latent factor model and extend it to a heterogeneous one, which models the interaction of different kinds of information. This latent model is called “Content + Attributes”, which incorporates latent topics and descriptive attributes using probabilistic matrix factorization and topic modeling to figure out the final recommendation results in heterogeneous scenarios. Moreover, we further propose a solution to handle the cold start problem of new users by adopting social structures. We conduct extensive experiments on the DBLP dataset and the experimental results show that our proposed model outperforms the baseline methods.


Recommendation System Matrix Factorization Topic Modeling Social Network Service Cold Start Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chenyi Zhang
    • 1
    • 3
  • Xueyi Zhao
    • 2
  • Ke Wang
    • 3
  • Jianling Sun
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityChina
  2. 2.Dept. of Information Science and Electronic EngineeringZhejiang UniversityChina
  3. 3.School of Computing ScienceSimon Fraser UniversityCanada

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