A Recommendation System with the Use of Comprehensive Trend Indication Based on Weighted Complete Graph
Takuya Sugimoto*, Tetsuya Toyota*,**,
and Hajime Nobuhara*
*Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tenodai, Tsukuba Science City, Ibaraki 305-8573, Japan
**Japan Society for the Promotion of Science, Sumitomo Ichibancho FS Bldg., 8 Ichibancho, Chiyoda-ku, Tokyo 102-8472, Japan
Recently, Internet shopping has become widespread, websites of which are equipped with a recommendation system to help users easily find their target items from among vast product information. As a typical method to create recommendation information, collaborative filtering is used but it has a problem that recommendation results tend to be biased toward the same category. Since this study intends recommendation with a high discoverability from a large point of view of category, we define dissimilarity between products based on information on Browse Node ID held by some products in Amazon and use k-medoids to newly categorize the products. Moreover, we create a weighted complete graph with those categories as nodes and indicate the trend across different categories. The proposed system estimates and recommends a category strongly related to a category that is thought to be unknown to the user but the user will like based on information of the weighted complete graph. We evaluate the effectiveness of the proposed system through experiments with 9 undergraduate students, 12 graduate students, and 2 office workers as subjects and show that the proposed system is better in recommending unknown products to the user than existing recommendation systems.
-  Compete, “Compete Releases Top 25 Retail Web Sites for July 2009,” 2009.
-  S. Alag, “Collective Intelligence in Action,” Manning Publications, 2008.
-  P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” CSCW ’94 Proc. of the 1994 ACM Conf. on Computer supported cooperative work, 1994.
-  U. Shardanand and P. Maes, “Social information filtering: algorithms for automating “word of mouth”,” CHI ’95 Proc. of the SIGCHI Conf. on Human factors in computing systems, 1995.
-  T. Akinaga, N. Ohsugi, M. Tsunoda, T. Kakimoto, A. Monden, and K. Matsumoto, “Recommendation of Software Technologies Based on Collaborative Filtering,” Asia-Pacific Software Engineering Conf., pp. 209-216, 2005.
-  C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” Proc. of the 14th Int. Conf. on World Wide Web, pp. 22-32, 2005.
-  Y. Hijikata, T. Shimizu, and S. Nishida, “Discovery-oriented collaborative filtering for improving user satisfaction,” Proc. of the 14th Int. Conf. on Intelligent user interfaces, pp. 67-76, 2009.
-  Amazon.com, Inc., “Amazon Web Services.”
-  H. Takamura, “Introduction to Machine Learning for Natural Language Processing,” Corona Publishing Co., LTD., 2010.
-  S. Theodoridis and K. Koutroumbas, “Pattern Recognition, Third Edition,” Academic Press, 2006.
-  S. Theodoridis and K. Koutroumbas, “Pattern Recognition, Fourth Edition,” Academic Press, 2008.
-  D. Gries and F. B. Schneider, “A Logical Approach to Discrete Math (Monographs in Computer Science),” Springer, 1993.
-  Laboratory of DNA Information Analysis Human Genome Center Institute of Medical Science University of Tokyo, “Open source Clustering software.”
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.