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 |
|---|---|
| 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 | |
| Publication status | Published - 2017 |
| Event | 26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia Duration: 2017 Apr 3 → 2017 Apr 7 |
Publication series
| Name | 26th International World Wide Web Conference 2017, WWW 2017 Companion |
|---|
Other
| Other | 26th International World Wide Web Conference, WWW 2017 Companion |
|---|---|
| Country/Territory | Australia |
| City | Perth |
| Period | 17/4/3 → 17/4/7 |
Bibliographical note
Funding Information:This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2014R1A2A1A10054151).
Publisher Copyright:
© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
All Science Journal Classification (ASJC) codes
- Software
- Computer Networks and Communications