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An improved collaborative filtering method based on similarity

Author

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  • Junmei Feng
  • Xiaoyi Fengs
  • Ning Zhang
  • Jinye Peng

Abstract

The recommender system is widely used in the field of e-commerce and plays an important role in guiding customers to make smart decisions. Although many algorithms are available in the recommender system, collaborative filtering is still one of the most used and successful recommendation technologies. In collaborative filtering, similarity calculation is the main issue. In order to improve the accuracy and quality of recommendations, we proposed an improved similarity model, which takes three impact factors of similarity into account to minimize the deviation of similarity calculation. Compared with the traditional similarity measure, the advantages of our proposed model are that it makes full use of rating data and solves the problem of co-rated items. To validate the efficiency of the proposed algorithm, experiments were performed on four datasets. Results show that the proposed method can effectively improve the preferences of the recommender system and it is suitable for the sparsity data.

Suggested Citation

  • Junmei Feng & Xiaoyi Fengs & Ning Zhang & Jinye Peng, 2018. "An improved collaborative filtering method based on similarity," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0204003
    DOI: 10.1371/journal.pone.0204003
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    References listed on IDEAS

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    1. Gediminas Adomavicius & Jingjing Zhang, 2016. "Classification, Ranking, and Top-K Stability of Recommendation Algorithms," INFORMS Journal on Computing, INFORMS, vol. 28(1), pages 129-147, February.
    2. Shuang-Bo Sun & Zhi-Heng Zhang & Xin-Ling Dong & Heng-Ru Zhang & Tong-Jun Li & Lin Zhang & Fan Min, 2017. "Integrating Triangle and Jaccard similarities for recommendation," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    3. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
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    1. Latha, R., 2022. "Enhancing recommendation competence in nearest neighbour models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).

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