Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning.
Dusan Stamenkovic|Alexandros Karatzoglou|Ioannis Arapakis|Xin Xin|Kleomenis Katevas
| Anthology ID: | DBLP:conf/wsdm/StamenkovicKAXK22 |
|---|---|
| Volume: | WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022 |
| Year: | 2022 |
| Venue: | Web Search and Data Mining (WSDM) |
| Publisher: | ACM |
| Pages: | 957-965 |
| URL: | https://doi.org/10.1145/3488560.3498471 |
| DOI: | https://doi.org/10.1145/3488560.3498471 |
| DBLP: | conf/wsdm/StamenkovicKAXK22 |
| BibTeX: |
@inproceedings{stamenkovic-2022-choosing,
author = {Dusan Stamenkovic and
Alexandros Karatzoglou and
Ioannis Arapakis and
Xin Xin and
Kleomenis Katevas},
editor = {K. Sel\c{c}uk Candan and
Huan Liu and
Leman Akoglu and
Xin Dong and
Jiliang Tang},
title = {{Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning}},
booktitle = {{WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022}},
pages = {957--965},
publisher = {ACM},
year = {2022},
url = {https://doi.org/10.1145/3488560.3498471},
doi = {https://doi.org/10.1145/3488560.3498471}
}