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Introduction
Shiqing is an Assistant Professor in the Faculty of Data Science at the City University of Macau. His research interests involve incentive allocation problem, influence diffusion analysis, agent-based modeling, and recommender systems.
Current institution
Additional affiliations
July 2022 - December 2024
October 2019 - August 2022
February 2017 - October 2019
Publications
Publications (31)
AI recommendation techniques provide users with personalized services, feeding them the information they may be interested in. The increasing personalization raises the hypotheses of the "filter bubble" and "echo chamber" effects. To investigate these hypotheses, in this paper, we inspect the impact of recommendation algorithms on forming two types...
With the success of Bitcoin, nowadays, both academia and industry have been focusing on the expansion of blockchain in non-cryptocurrency applications to facilitate the development of Industry 4.0. The incentive mechanism in blockchain, which encourages miners to participate in validation processes, is important to the maintenance of the system. Ho...
Graph neural network (GNN) based algorithms have achieved superior performance in recommendation tasks due to their advanced capability of exploiting high-order connectivity between users and items. However, most existing GNN-based recommendation models ignore the dynamic evolution of nodes, where users will continuously interact with items over ti...
Improving users’ long-term experience in recommender systems (RS) has become a growing concern for recommendation platforms. Reinforcement learning (RL) is an attractive approach because it can plan and optimize long-term returns sequentially. However, directly applying RL as an online learning method in the RS setting can significantly compromise u...
Temporal knowledge graph (TKG) forecasting is widely used in various fields due to its ability to infer future events based on historical information. Modeling the internal structures and chronological dependencies of historical subgraph sequences has been proven effective. Nevertheless, on the one hand, the TKG forecasting process generally suffers...
Fairness in recommendation has drawn much attention since it significantly affects how users access information and how information is exposed to users. However, most fairness-aware methods are designed offline with the entire stationary interaction data to handle the global unfairness issue and evaluate their performance in a one-time paradigm. In...
The rapid development of IoT, cloud computing, and big data has led to an exponential increase in data complexity, driving the widespread application of complex networks. In transportation networks, for example, accurately predicting vehicle behaviors and traffic flow is critical for optimizing intelligent transportation systems. However, tradition...
Dynamic recommendation systems, where users interact with items continuously over time, have been widely deployed in real-world online streaming applications. The burst of interaction stream causes a rapid evolution of both users and items. To update representations dynamically, existing studies have investigated event-level and history-level dynam...
Personalized recommendation systems homogenize user preferences, causing an extreme belief imbalance and aggravating user bias. This phenomenon is known as the filter bubble. This paper presents the Responsible Graph-based Recommendation (RGRec) system, designed to alleviate the filter bubble effect in personalized recommendation systems. Acting as...
The phenomenon known as the “echo chamber” has been widely acknowledged as a significant force affecting society. This has been particularly evident during the Covid-19 pandemic, wherein the echo chamber effect has significantly influenced public responses. Therefore, detecting echo chambers and mitigating their adverse impacts has become crucial t...
As online social media platforms continue to proliferate, users are faced with an overwhelming amount of information, making it challenging to filter and locate relevant information. While personalized recommendation algorithms have been developed to help, most existing models primarily rely on user behavior observations such as viewing history, of...
In the realm of personalized recommendation systems, the increasing concern is the amplification of belief imbalance and user biases, a phenomenon primarily attributed to the filter bubble. Addressing this critical issue, we introduce an innovative intermediate agency (BHEISR) between users and existing recommendation systems to attenuate the negat...
Online social media platforms offer access to a vast amount of information, but sifting through the abundance of news can be overwhelming and tiring for readers. personalised recommendation algorithms can help users find information that interests them. However, most existing models rely solely on observations of user behaviour, such as viewing his...
Influence maximization is recognized as a crucial optimization problem, which aims to identify a limited set of influencers to maximize the coverage of influence dissemination in social networks. However, real-world social networks are usually dynamic and large-scale, which leads to difficulty in capturing real-time user and diffusion features to e...
In recent years, recommenze the social influence among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers in the social network are possibly incentivized indirectly. However, in many real-world applications, identifying influential users can be challenging because of the unknown network...
In recent years, many applications have deployed incentive mechanisms to promote users’ attention and engagement. Most incentive mechanisms determine specific incentive values based on users’ attributes (e.g., preferences), while such information is unavailable in many real-world applications. Meanwhile, due to budget restrictions, realizing succes...
In recent years, providing incentives to human users for attracting their attention and engagement has been widely adopted in many applications. To effectively incentivize users, most incentive mechanisms determine incentive values based on users' individual attributes, such as preferences. These approaches could be ineffective when such informatio...
Influence diffusion modelling in online social networks has been widely studied and applied in public opinion management, viral marketing, and rumour detection. Most existing studies focus on the network topology and the complex user characteristics while ignoring the diverse topic features of the information, especially the cross-impact of multipl...
In recent years, recommendation systems have been widely applied in many domains. These systems are impotent in affecting users to choose the behavior that the system expects. Meanwhile, providing incentives has been proven to be a more proactive way to affect users' behaviors. Due to the budget limitation, the number of users who can be incentiviz...
Social media have dramatically changed the mode of information dissemination. Various models and algorithms have been developed to model information diffusion and address the influence maximization problem in complex social networks. However, it appears difficult for state-of-the-art models to interpret complex and reversible real interactive netwo...
Most existing incentive allocation approaches rely on sufficient information about users' attributes, such as their preferences, followers in the social network, and activities, to customize effective incentives. However, this may lead to failure when such knowledge is unavailable. In this light, we propose an end-to-end reinforcement learning-base...
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. The evolving nature of the topological structure of these networks makes it difficult to locate and identify these influencers. In this paper, we p...
In recent years, more and more systems have been designed to affect users’ decisions for realizing certain system goals. However, most of these systems only focus on affecting users’ short-term or one-off behaviors, while ignoring the maintenance of users’ long-term engagement. In this light, we intend to design a novel approach which focuses on in...
Most recommendation systems are designed for seeking users’ demands and preferences, whereas impotent to affect users’ decisions for realizing the system-level objective. In this light, we intend to propose a generic concept named ‘proactive recommendation’, which focuses on not only maintaining users’ satisfaction but also realizing system-level o...
Negative impacts produced by transportation sector have increased in parallel with the increase of urban mobility. In this paper, we introduce GreenCommute, a novel recommendation system which can facilitate commuters to take public friendly commute options, while provide support to alleviate the external cost in society, such as traffic pollution,...
