Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152719
Title: A hybrid bandit framework for diversified recommendation
Authors: Ding, Qinxu
Liu, Yong
Miao, Chunyan
Cheng, Fei
Tang, Haihong
Keywords: Engineering::Computer science and engineering
Engineering::Computer science and engineering::Information systems::Information storage and retrieval
Issue Date: 2021
Source: Ding, Q., Liu, Y., Miao, C., Cheng, F. & Tang, H. (2021). A hybrid bandit framework for diversified recommendation. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 35, 4036-4044.
Project: Alibaba-NTU-AIR2019B1
AISG-GC-2019-003
NRF-NRFI05- 2019-0002
MOH/NIC/COG04/2017
MOH/NIC/HAIG03/2017
Abstract: The interactive recommender systems involve users in the recommendation procedure by receiving timely user feedback to update the recommendation policy. Therefore, they are widely used in real application scenarios. Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set. However, the investigation of users' personalized preferences on the diversity properties of an item set is usually ignored. To overcome this problem, we propose the Linear Modular Dispersion Bandit (LMDB) framework, which is an online learning setting for optimizing a combination of modular functions and dispersion functions. Specifically, LMDB employs modular functions to model the relevance properties of each item, and dispersion functions to describe the diversity properties of an item set. Moreover, we also develop a learning algorithm, called Linear Modular Dispersion Hybrid (LMDH) to solve the LMDB problem and derive a gap-free bound on its n-step regret. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed LMDB framework in balancing the recommendation accuracy and diversity.
URI: https://hdl.handle.net/10356/152719
ISBN: 978-1-57735-866-4
ISSN: 2159-5399
Rights: © 2021 Association for the Advancement of Artificial Intelligence. All Rights Reserved. This paper was published in Proceedings of Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) and is made available with permission of Association for the Advancement of Artificial Intelligence.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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