A Ranked Bandit Approach for Multi-stakeholder Recommender Systems
DocUID: 2022-008 Full Text:
PDF Author: Tahereh Arabghalizi, Alexandros Labrinidis
Abstract: Recommender systems traditionally find the most relevant products or services for users tailored to their needs or interests but they ignore the interests of the other sides of the market (aka stakeholders). In this paper, we propose to use a Ranked Bandit approach for an online multi-stakeholder recommender system that sequentially selects top š items according to the relevance and priority of all the involved stakeholders. We presented three different criteria to consider the priority of each stakeholder when evaluating our approach. Our extensive experimental results on a movie dataset showed that the contextual multi-armed bandits with a relevance function make a higher level of satisfaction for all involved stakeholders in the long term. Keywords: Multi-stakeholder Recommender Systems; Multi-armed Bandits; Ranked Bandit;
Keywords: Multi-stakeholder Recommender Systems; Multi-armed Bandits; Ranked Bandit;
Published In: Workshop of Multi-Objective Recommender Systems (MORSā22), in conjunction with the 16th ACM Conference on Recommender Systems, RecSys
Pages: 22
Year Published: 2022
Project: PittSmartLiving Subject Area: Multi-armed Bandits, Recommender Systems
Publication Type: Workshop Paper
Sponsor: NSF CNS-1739413