A Design of Recommendation Based on Flexible Mixture Model Considering Purchasing Interest and Post-Purchase Satisfaction

• SUZUKI Takeshi

  Graduate School of Creative Science and Engineering, Waseda University

• KUMOI Gendo

  Research Institute for Science and Engineering, Waseda University

• MIKAWA Kenta

  Graduate School of Creative Science and Engineering, Waseda University

• GOTO Masayuki

  School of Creative Science and Engineering, Waseda University

Abstract

The recommender system is an effective Web marketing tool that havve been used especially on electric commerce sites in recent years. The recommender system provides each user with a list of new recommended items that are predicted to be preferred by the user. Collaborative filtering is one of the most representative and powerful methods to predict user preference in the recommender system. Collaborative filtering measures the similarity of preference between users and uses it to decide items to be recommended. Based on previous researche on this method, user preference is considered to have two aspects: Purchasing interest for items and post-purchase satisfaction with items. However, the conventional methods do not consider the two different preferences at the same time. This paper suggests taking these two preferences into account and proposes a new method that allows users to choose the balance between them. The proposed method is evaluated through simulation experiments with MovieLens data. It demonstrates the effectiveness of our proposal in precision and average rating compared with a previous method.

Journal

• Journal of Japan Industrial Management Association

  Journal of Japan Industrial Management Association 64 (4E), 570-578, 2014

  Japan Industrial Management Association

Keywords

• recommender systems
• collaborative filtering
• flexible mixture model
• probabilistic models
• latent class model

Details

• CRID: 1390282680481187456
• NII Article ID: 130005002540
• DOI: 10.11221/jima.64.570
• ISSN: 21879079
• ISSN: 13422618
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed