Dimension Reduction Based on Effects of Experienced Users in Recommender Systems

  • Bo ChenEmail author
  • Xiaoqian Lu
  • Jian He
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


The paradox of huge volume with high sparsity of rating data in collaborative filtering (CF) system motivates the present paper to utilize information underlying sparsity to reduce the dimensionality of data. This difference in user experiences resembles factor underlying widely used term frequency weighting scheme in information retrieval. Hypothesis of Rational Authorities Bias (H-RAB) is proposed, supposing that higher prediction accuracy can be attained to emphasize referential users with higher experiences. Dimension reduction suggests pruning all referential users with less experience than a given maturity threshold. Empirical results from a series of experiments on three major public available CF datasets justify the soundness of both modifications and validity of H-RAB. A few open issues are also proposed for future efforts.


Collaborative filtering recommender Sparsity Rater maturity Rational Authorities Bias Dimension reduction 



This work is partly funded by UESTC Fundamental Research Funds for Central Universities (ZYGX2015J069), Grant: JSEB-201303 of e-Commerce Key Laboratory of Jiangsu Province, and Grant 2017GZYZF0014 of Sichuan Sci-Tech Plan.


  1. 1.
    Sparck Jones K. Index term weighting. Inf Storage Retrieval. 1973;9:619–33.CrossRefGoogle Scholar
  2. 2.
    Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th conference on uncertainty in artificial intelligence; 1998. p. 43–52.Google Scholar
  3. 3.
    McLaughlin MR, Herlocker JL. A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval (Sheffield, United Kingdom, July 25 – 29, 2004). SIGIR’04; New York: ACM Press; 2004. p.329–36.Google Scholar
  4. 4.
    Goldberg K, Roeder T, Gupta D, Perkins C. Eigentaste: a constant time collaborative filtering algorithm. Inf Retrieval. 2001;4(2):133–51.CrossRefGoogle Scholar
  5. 5.
    Berry MW, Dumais ST, O’Brien GW. Using linear algebra for intelligent information retrieval. SIAM Rev. 1995;37(4):573–95.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R. Indexing by latent semantic analysis. J Am Soc Inform Sci. 1990;41(6):391–407.CrossRefGoogle Scholar
  7. 7.
    Billsus D, Pazzani MJ. Learning collaborative information filters. In: Proceedings of the 15th international conference on machine learning; 1998. San Francisco, CA: Morgan Kaufmann. p. 46–54.Google Scholar
  8. 8.
    Herlocker Jl, Konstan JA, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval; 1999; Berkeley, California, United States. p. 230–7.Google Scholar
  9. 9.
    Herlocker JL, Konstan JA, Terveen LG, Riedl J. Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst. 2004;22(1):5–53.CrossRefGoogle Scholar
  10. 10.
    Sarwar B, Karypis, G, Konstan J, Riedl J. Item-Based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international world wide web conference; 2001. p. 285–95.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information and Software Engineering, University of Electronic Science & Technology of ChinaChengduChina
  2. 2.Information Center, University of Electronic Science & Technology of ChinaChengduChina
  3. 3.School of Automation Engineering, University of Electronic Science & Technology of ChinaChengduChina

Personalised recommendations