Mixed Recommendation Algorithm Based on Commodity Gene and Genetic Algorithm

  • Zhang Hao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 219)


To solve the problems of “new user” and “sparseness”, we introduce the concept of commodity gene. Through coupling the commodity gene database, users’ purchasing historical records, users’ content of online browsing and the data of neighbors’ behavior, we can form the module of candidate sets of customer preferences, and then use genetic algorithm which has been improved to make the selection and polymerization to the model, so that we can complete the best selection of neighbors. Finally, we can get the recommended sets according to the recommended module. Experimental results show that the algorithm we suggested can improve the accuracy of the recommendation and achieve good quality of recommendation.


Commodity gene Recommendation algorithm Genetic algorithm 


  1. 1.
    Sarwar B, Karypis G, Konstan J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international World Wide Web conference vol 52, pp. 285–295Google Scholar
  2. 2.
    Herlocker J, Konstan J, Borchers A et al (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the conference on research and development in information retrieval, vol 578, pp. 230–237 Google Scholar
  3. 3.
    Baglioni M, Ferrara U, Romei A (2003) Preprocessing and mining web log data for web personalization. Adv Artif Intell 2:237–249Google Scholar
  4. 4.
    Raymond kosala, Hendrik Blockeel (2000) Web mining research: a survey SIGKDD explorations. ACM SIGKDD 2(1):1–15CrossRefGoogle Scholar
  5. 5.
    Sung HM, Ingoo H (2005) Optimizing collaborative filtering recommender systems. ACM Trans Inf Syst (TOIS) 22(1):313–319Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Department of Transportation EngineeringHuaiyin Institute of TechnologyHuai’anChina

Personalised recommendations