A Degree-Based Method to Solve Cold-Start Problem in Network-Based Recommendation

  • Yong Liu
  • Fan Jia
  • Wei Cao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)


Recommender systems have become increasingly essential in fields where mass personalization is highly valued. In this paper, we propose a model based on the analysis of the similarity between the new item and the object that the users have selected to solve cold-start problem in network-based recommendation. In order to improve the accuracy of the model, we take the degree of the items that have been collected by the user into consideration. The experiments with MovieLens data set indicate substantial improvements of this model in overcoming the cold-start problem in network-based recommendation.


Recommender systems Network-based filtering Similarity Item degree Cold-start 



This work has been supported by the National Natural Science Foundation of China under Grant 61172072, 61271308, the Fundamental Research Funds for the Central Universities under Grant 2013JBM006, the Beijing Natural Science Foundation under Grant 4112045, the Research Fund for the Doctoral Program of Higher Education of China under Grant W11C100030, the Beijing Science and Technology Program under Grant Z121100000312024. The authors are also grateful for the comments and suggestions of the reviewers.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  2. 2.Key Laboratory of Communication and Information SystemsBeijing Municipal Commission of Education, Beijing Jiaotong UniversityBeijingChina
  3. 3.China Information Technology Security Evaluation CenterBeijingChina

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