Abstract
This paper addresses recommendation diversification. Existing diversification methods have a difficulty in dealing with the accuracy-diversity tradeoff. We propose a novel method to simultaneously optimize the user preference and diversity of k-items to be recommended.
Original language | English |
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Title of host publication | 26th International World Wide Web Conference 2017, WWW 2017 Companion |
Publisher | International World Wide Web Conferences Steering Committee |
Pages | 809-810 |
Number of pages | 2 |
ISBN (Electronic) | 9781450349147 |
DOIs | |
State | Published - 2017 |
Event | 26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia Duration: 3 Apr 2017 → 7 Apr 2017 |
Publication series
Name | 26th International World Wide Web Conference 2017, WWW 2017 Companion |
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Conference
Conference | 26th International World Wide Web Conference, WWW 2017 Companion |
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Country/Territory | Australia |
City | Perth |
Period | 3/04/17 → 7/04/17 |
Bibliographical note
Publisher Copyright:© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
Keywords
- Diversification
- E-commerce
- Recommender system