Journal of Korea Society of Digital Industry and Information Management (디지털산업정보학회논문지)

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
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An Collaborative Filtering Method based on Associative Cluster Optimization for Recommendation System

추천시스템을 위한 연관군집 최적화 기반 협력적 필터링 방법

  • 이현진 (한국사이버대학교 컴퓨터정보통신학과) ;
  • 지태창 (연세대학교 컴퓨터과학과)
  • Received : 2010.07.23
  • Accepted : 2010.09.05
  • Published : 2010.09.30
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Abstract

A marketing model is changed from a customer acquisition to customer retention and it is being moved to a way that enhances the quality of customer interaction to add value to our customers. Such personalization is emerging from this background. The Web site is accelerate the adoption of a personalization, and in contrast to the rapid growth of data, quantitative analytical experience is required. For the automated analysis of large amounts of data and the results must be passed in real time of personalization has been interested in technical problems. A recommendation algorithm is an algorithm for the implementation of personalization, which predict whether the customer preferences and purchasing using the database with new customers interested or likely to purchase. As recommended number of users increases, the algorithm increases recommendation time is the problem. In this paper, to solve this problem, a recommendation system based on clustering and dimensionality reduction is proposed. First, clusters customers with such an orientation, then shrink the dimensions of the relationship between customers to low dimensional space. Because finding neighbors for recommendations is performed at low dimensional space, the computation time is greatly reduced.

Keywords

References

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