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.
|Title of host publication||26th International World Wide Web Conference 2017, WWW 2017 Companion|
|Publisher||International World Wide Web Conferences Steering Committee|
|Number of pages||2|
|Publication status||Published - 2017|
|Event||26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia|
Duration: 2017 Apr 3 → 2017 Apr 7
|Name||26th International World Wide Web Conference 2017, WWW 2017 Companion|
|Other||26th International World Wide Web Conference, WWW 2017 Companion|
|Period||17/4/3 → 17/4/7|
Bibliographical noteFunding Information:
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2014R1A2A1A10054151).
© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications