DAC-GCN: A Dual Actor-Critic Graph Convolutional Network with Multi-Hop Aggregation for Enhanced Recommender Systems

DAC-GCN: A Dual Actor-Critic Graph Convolutional Network with Multi-Hop Aggregation for Enhanced Recommender Systems

Background: Recommender Systems (RSs) frequently face challenges in balancing exploration and exploitation, particularly in dynamic environments where user behaviors evolve over time. Traditional methods struggle to adapt to these complexities, limiting their effectiveness in real-world domains such...

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Bibliographic Details
Main Authors: Gholamreza Zare, Nima Jafari, Mehdi Hosseinzadeh, Amir Sahafi
Format: Article
Language:English
Published: Prague University of Economics and Business 2025-08-01
Series:Acta Informatica Pragensia
Subjects:
recommender system
graph convolutional network
actor-critic
reinforcement learning
multi-hop aggregation
personalized recommendations
Online Access:https://aip.vse.cz/artkey/aip-202503-0004_dac-gcn-a-dual-actor-critic-graph-convolutional-network-with-multi-hop-aggregation-for-enhanced-recommender-sy.php
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https://aip.vse.cz/artkey/aip-202503-0004_dac-gcn-a-dual-actor-critic-graph-convolutional-network-with-multi-hop-aggregation-for-enhanced-recommender-sy.php

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