no code implementations • 7 Apr 2024 • Mengyan Wang, Yuxuan Hu, Shiqing Wu, Weihua Li, Quan Bai, Verica Rupar
While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of ``filter bubbles''.
no code implementations • 6 Jul 2023 • Mengyan Wang, Yuxuan Hu, Zihan Yuan, Chenting Jiang, Weihua Li, Shiqing Wu, Quan Bai
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.
no code implementations • 2 Feb 2023 • Mengyan Wang, Weihua Li, Jingli Shi, Shiqing Wu, Quan Bai
In this paper, we propose a novel method to address the complex problem of news recommendation.
no code implementations • 7 Nov 2022 • Qing Liu, Wenli Yang, Shiqing Wu
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).
1 code implementation • 17 Mar 2022 • Shiqing Wu, Weihua Li, Quan Bai
The experimental results indicate that GAC can learn and apply effective incentive allocation policies in unknown social networks and outperform existing incentive allocation approaches.
no code implementations • 13 Jul 2021 • Shiqing Wu, Weihua Li, Hao Shen, Quan Bai
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.
no code implementations • 14 Apr 2021 • Weihua Li, Yuxuan Hu, Shiqing Wu, Quan Bai, Edmund Lai
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.