Product Recommendation System Based on Personal Preference Model Using CAM

• Murakami Tomoko

  Knowledge Media Laboratory, Corporate Research & Development Center, Toshiba Graduate of Media and Governance, Keio University

• Yoshioka Nobukazu

  National Institute of Informatics

• Orihara Ryohei

  Knowledge Media Laboratory, Corporate Research & Development Center, Toshiba

• Furukawa Koichi

  Graduate of Media and Governance, Keio University

Bibliographic Information

Other Title

• ＣＡＭ法を用いた個人し好モデルに基づく商品推薦システム
• CAM法を用いた個人嗜好モデルに基づく商品推薦システム
• CAMホウ オ モチイタ コジンシコウモデル ニ モトヅク ショウヒン スイセン システム

Abstract

Product recommendation system is realized by applying business rules acquired by data maining techniques. Business rules such as demographical patterns of purchase, are able to cover the groups of users that have a tendency to purchase products, but it is difficult to recommend products adaptive to various personal preferences only by utilizing them. In addition to that, it is very costly to gather the large volume of high quality survey data, which is necessary for good recommendation based on personal preference model. A method collecting kansei information automatically without questionnaire survey is required. The constructing personal preference model from less favor data is also necessary, since it is costly for the user to input favor data. <BR> In this paper, we propose product recommendation system based on kansei information extracted by text mining and user's preference model constructed by Category-guided Adaptive Modeling, CAM for short. CAM is a feature construction method that can generate new features constructing the space where same labeled examples are close and different labeled examples are far away from some labeled examples. <BR> It is possible to construct personal preference model by CAM despite less information of likes and dislikes categories. In the system, retrieval agent gathers the products' specification and user agent manages preference model, user's likes and dislikes. Kansei information of the products is gained by applying text mining technique to the reputation documents about the products on the web site. We carry out some experimental studies to make sure that prefrence model obtained by our method performs effectively.

Journal

• Transactions of the Japanese Society for Artificial Intelligence

  Transactions of the Japanese Society for Artificial Intelligence 20 346-355, 2005

  The Japanese Society for Artificial Intelligence

Keywords

• recommendation system
• feature construction
• kansei
• text mining

Details

• CRID: 1390282680084656512
• NII Article ID: 10022005507
• NII Book ID: AA11579226
• DOI: 10.1527/tjsai.20.346
• ISSN: 13468030
• ISSN: 13460714
• NDL BIB ID: 8685338
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I8685338
• Web Site: http://www.jstage.jst.go.jp/article/tjsai/20/5/20_5_346/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed