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We introduce reinforcement learning to the multi-behavior session-based recommendation task and propose a novel approach called the multi-behavior graph ...
• We propose Multi-behavior Graph Reinforcement Learning Network (MB-GRL), a novel method that combines graph neural networks and reinforcement learning to.
Feb 17, 2023 · Session-based recommendation with graph neural networks. ... Recommenda- tions with negative feedback via pairwise deep reinforcement learning.
Sep 3, 2024 · We design a Multi-behavior User Intent Recommendation model (MUIR) to produce a recommendation to explore the complicated item-item dependencies with multiple ...
Missing: via Reinforcement
Aug 16, 2023 · This paper proposes a multi-behavior recommendation framework based on a graph neural network, which captures personalized semantics of specific behavior.
Missing: Reinforcement | Show results with:Reinforcement
Multi-behavior session-based recommendation aims to predict the next item, such as a location-based service (LBS) or a product, to be interacted by a ...
Missing: Reinforcement | Show results with:Reinforcement
This repository collects the latest research progress of Contrastive Learning (CL) and Data Augmentation (DA) in Recommender Systems.
May 27, 2024 · In the MBSR problem, the model is given an additional sequence of behaviors alongside the item sequence to predict the next interacted item.
GraphCrop is parameter learning free and easy to implement within existing GNN-based graph classifiers. Qualitatively, GraphCrop expands the existing training ...
Missing: Reinforcement | Show results with:Reinforcement
Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. In Proceedings of the 43rd International ACM ...