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eNauka >  Results >  Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Title: Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Authors: Stamenkovic, Dusan; Karatzoglou, Alexandros; Arapakis, Ioannis; Xin, Xin; Katevas, Kleomenis
Issue Date: 2022
Publication: WSDM'22: PROCEEDINGS OF THE FIFTEENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING
Type: Conference Paper
Collation: str. 957-965
DOI: 10.1145/3488560.3498471
WoS-ID: 000810504300102
Scopus-ID: 2-s2.0-85125779242
URI: https://enauka.gov.rs/handle/123456789/800878
Project: Natural Science Foundation of China [62072279]
National Key R&D Program of China [2020YFB1406704]
Fundamental Research Funds of Shandong University
Metadata source: (Preuzeto iz Nasi u WoS)
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