International Conference on Collaborative Computing: Networking, Applications and Worksharing

Collaborative Computing: Networking, Applications, and Worksharing pp 296-302 | Cite as

LTMF: Local-Based Tag Integration Model for Recommendation

  • Deyuan Zheng
  • Huan Huo
  • Shang-ye Chen
  • Biao Xu
  • Liang Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 163)


There are two primary approaches to collaborative filtering: memory- based and model-based. The traditional techniques fail to integrate with these two approaches and also can’t fully utilize the tag features which data contains. Based on mining local information, this paper combines neighborhood method and matrix factorization technique. By taking fuller consideration of the tag features, we propose an algorithm named LTMF (Local-Tag MF). After the real data validation, this model performs better than other state-of-art algorithms.



This work is supported by National Natural Science Foundation of China (61003031, 61202376), Shanghai Engineering Research Center Project (GCZX14014), Shanghai Key Science and Technology Project in IT(14511107902), Shanghai Leading Academic Discipline Project(XTKX2012) and Hujiang Research Center Special Foundation(C14001).


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Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Deyuan Zheng
    • 1
  • Huan Huo
    • 1
  • Shang-ye Chen
    • 2
  • Biao Xu
    • 1
  • Liang Liu
    • 1
  1. 1.University of Shanghai for Science and TechnologyShanghaiChina
  2. 2.School of Information and TechnologyNorthwest UniversityXianChina

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