A Comparison of Dimensionality Reduction Algorithms for Improving Efficiency of PromoRank

  • Metawat Kavilkrue
  • Pruet Boonma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8839)


Promotion plays a crucial role in online marketing, which can be used in post-sale recommendation, developing brand, customer support, etc. It is often desirable to find markets or sale channels where an object, e.g., a product, person or service, can be promoted efficiently. For example, when a client borrows a book from a library, the library might want to suggest another related books to them based on their interest. However, since the object, e.g., book, may not be highly ranked in the global property space, PromoRank algorithm promotes a given object by discovering subspaces in which the target is top rank. Nevertheless, the computation complexity of PromoRank is exponential to the dimension of the space. This paper proposes to use dimensionality reduction algorithms, such as PCA or FA, in order to reduce the dimension size and, as a consequence, improve the performance of PromoRank. This paper evaluates multiple dimensionality reduction algorithms to obtain the understanding about the relationship between properties of data sets and algorithms such that an appropriate algorithm can be selected for a particular data set.


Principal Component Analysis Target Object Graduation Rate Grad Rate Object Count 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kotler, P., Keller, K.: Marketing Management. Prentice Hall (2008)Google Scholar
  2. 2.
    Wang, J., Zhang, Y.: Opportunity model for e-commerce recommendation: Right product; right time. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 303–312. ACM, New York (2013)Google Scholar
  3. 3.
    Wu, T., Xin, D., Mei, Q., Han, J.: Promotion analysis in multi-dimensional space. In: International Conference on Very Large Databases, France. VLDB Endowment (2009)Google Scholar
  4. 4.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: ACM Conference on Recommender Systems, Lausanne, Switzerland. ACM (2008)Google Scholar
  5. 5.
    Kamishima, T., Akaho, S.: Dimension reduction for supervised ordering. In: International Conference on Data Mining, Hong Kong. IEEE Press (2006)Google Scholar
  6. 6.
    Ahn, H.J., Kim, J.W.: Feature reduction for product recommendation in internet shopping malls. International Journal of Electronic Business 4(5), 432–444 (2006)CrossRefGoogle Scholar
  7. 7.
    Fodor, I.: A survey of dimension reduction techniques. Technical report, Center for Applied Scientific Computing, Lawrence Livermore National Research Laboratory (2002)Google Scholar
  8. 8.
    Ailon, N., Chazelle, B.: Faster dimension deduction. Commun. ACM 53(2), 97–104 (2010)CrossRefGoogle Scholar
  9. 9.
    Forsythe, G.E., Henrici, P.: The cyclic Jacobi method for computing the principal values of a complex matrix. Transactions of the American Mathematical Society, 1–23 (1960)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Metawat Kavilkrue
    • 1
  • Pruet Boonma
    • 1
  1. 1.Faculty of EngineeringChiang Mai UniversityChiang MaiThailand

Personalised recommendations