An improved recommendation algorithm in collaborative filtering

Taek Hun Kim, Young Suk Ryu, Seok In Park, Sung Bong Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)


In Electronic Commerce it is not easy for customers to find the best suitable goods as more and more information is placed on line. In order to provide information of high value a customized recommender system is required. One of the typical information retrieval techniques for recommendation systems in Electronic Commerce is collaborative filtering which is based on the ratings of other customers who have similar preferences. However, collaborative filtering may not provide high quality recommendation because it does not consider customer's preferences on the attributes of an item and the preference is calculated only between a pair of customers. In this paper we present an improved recommendation algorithm for collaborative filtering. The algorithm uses the K-Means Clustering method to reduce the search space. It then utilizes a graph approach to the best cluster with respect to a given test customer in selecting the neighbors with higher similarities as well as lower similarities. The graph approach allows us to exploit the transitivity of similarities. The algorithm also considers the attributes of each item. In the experiment the EachMovie dataset of the Digital Equipment Corporation has been used. The experimental results show that our algorithm provides better recommendation than other methods.

Original languageEnglish
Title of host publicationE-Commerce and Web Technologies - Third International Conference, EC-Web 2002, Proceedings
EditorsKurt Bauknecht, A. Min Tjoa, Gerald Quirchmayr
PublisherSpringer Verlag
Number of pages8
ISBN (Print)3540441379, 9783540441373
Publication statusPublished - 2002
Event3rd International Conference on E-commerce and Web Technology, held in conjunction with the DEXA 02, EC-Web 2002 - Aix-en-Provence, France
Duration: 2002 Sept 22002 Sept 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2455 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other3rd International Conference on E-commerce and Web Technology, held in conjunction with the DEXA 02, EC-Web 2002

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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