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Modeling dynamic spatiotemporal user preference for location prediction: a mutually enhanced method
As the cornerstone of location-based services, location prediction aims to predict user’s next location through modeling user’s personal preference...
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Dynamic bipartite network model based on structure and preference features
Based on the complex network, the relationship in the real complex system can be modeled, and the bipartite network is a special complex network,...
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Backer Preference Modeling and Prediction of Crowdfunding Campaign Success
Crowdfunding market has witnessed rapid growth; however, the overall success rate remains relatively low. Therefore, predicting crowdfunding success...
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Cognitive capacity and aesthetics: the influence of visual working memory on landscape ink painting preference
The appreciation of art is not solely influenced by the inherent qualities of a work, such as image complexity, but also by external factors related...
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Multi-objective deep reinforcement learning for crowd-aware robot navigation with dynamic human preference
The growing development of autonomous systems is driving the application of mobile robots in crowded environments. These scenarios often require...
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Fused User Preference Learning for Task Assignment in Mobile Crowdsourcing
With the development of GPS-enabled smart devices and wireless networks, mobile crowdsourcing (MCS) has received wide attention in assigning...
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Preference Weight Vector Adjustment Strategy Based Dynamic Multiobjective Optimization
The multiple objective functions or constraints of dynamic multi-objective optimization problems (DMOPs) generally vary over time. The...
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Preference communication in multi-objective normal-form games
We consider preference communication in two-player multi-objective normal-form games. In such games, the payoffs resulting from joint actions are...
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A novel user preference-aware content caching algorithm in mobile edge networks
One of the most important strategies used to mitigate the adverse impacts of traffic growth on mobile networks is caching. By caching at the edge,...
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Multi-objective RL with Preference Exploration
Traditional multi-objective reinforcement learning problems pay attention to the expected return of each objective under different preferences....
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Opinion Dynamics
The computational social science is a challenging interdisciplinary field of study, aimed at studying social phenomena by merging the traditional...
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A Monte Carlo manifold spectral clustering algorithm based on emotional preference and migratory behavior
Inspired by various behaviors of creatures in nature, numerous efficient bionic algorithms are designed for dealing with complex clustering problems....
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Musical preference in an online music community in China
Online music communities reflect and influence people’s music tastes, providing a detailed digital record of individuals’ behavior. There have been...
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TPEDTR: temporal preference embedding-based deep tourism recommendation with card transaction data
Recently, the recommender system has been raised as one of the essential research topics in smart tourism. The massive card transaction data...
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Online listing data and their interaction with market dynamics: evidence from Singapore during COVID-19
With the emergence of Property Technology, online listing data have drawn increasing interest in the field of real estate–related big data research....
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Repetition Dynamics-based Deep Learning Model for Next Basket Recommendation
Next Basket Recommendation system analyzes users’ past interactions to provide personalized recommendations. For a better understanding of the...
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Local Certification of Majority Dynamics
In majority voting dynamics, a group of n agents in a social network are asked for their preferred candidate in a future election between two...
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An overview of probabilistic preference decision-making based on bibliometric analysis
Probabilistic preference decision-making (PPDM) is a branch of uncertain decision-making, which identifies decision-makers’ preferences with...
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An adaptive decision-making system supported on user preference predictions for human–robot interactive communication
Adapting to dynamic environments is essential for artificial agents, especially those aiming to communicate with people interactively. In this...
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Likelihood-free inference in state-space models with unknown dynamics
Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but...
