Search Results for author:
Found 7 papers, 1 papers with code
Balancing Information Perception with Yin-Yang: Agent-Based Information Neutrality Model for Recommendation Systems
While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of ``filter bubbles''.
BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations
This approach endeavors to transcend the constraints of the filter bubble, enrich recommendation diversity, and strike a belief balance among users while also catering to user preferences and system-specific business requirements.
DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems
In this paper, we propose a novel method to address the complex problem of news recommendation.
Proceedings of Principle and practice of data and Knowledge Acquisition Workshop 2022 (PKAW 2022)
Over the past two decades, PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-arts in the area of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI).
GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks
The experimental results indicate that GAC can learn and apply effective incentive allocation policies in unknown social networks and outperform existing incentive allocation approaches.
Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction
To tackle the aforementioned challenges, in this paper, we propose a novel algorithm for exploring influential users in unknown networks, which can estimate the influential relationships among users based on their historical behaviors and without knowing the topology of the network.
ABEM: An Adaptive Agent-based Evolutionary Approach for Mining Influencers in Online Social Networks
A key step in influence maximization in online social networks is the identification of a small number of users, known as influencers, who are able to spread influence quickly and widely to other users.
