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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Lee, Hosub Kobsa, Alfred Univ Calif Irvine Donald Bren Sch Informat & Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9781509043279

With the advent of the Internet of Things (IoT), users are more likely to have privacy concerns since their personal information could be collected, analyzed, and utilized without notice by the networked IoT devices and services. Users may want to control all such activities by explicitly expressing their privacy preferences. However, it is becoming increasingly difficult for users to do so, not only because of the cognitive burden of continuously making privacy decisions for IoT services, but also because IoT devices have no, or only very restricted, user interfaces. Intelligent software helping users make better privacy decisions will be an important component of privacy preserving IoT environments. In order to construct such a component, we aim to verify whether it will be possible to computationally model and predict users' privacy preferences in IoT. To that end, we survey 172 participants in a simulated campuswide IoT environment about their privacy preferences regarding hypothetical personal information tracking scenarios. Then, we cluster the scenarios based on the survey responses, arriving at four clusters with distinct associated privacy preferences. Based on the clustering results, we uncover contextual factors that induce privacy violations in IoT. Finally, we build machine learning models to predict users' privacy decisions, using both contextual information and the corresponding cluster membership as training data. The final trained model shows 77% accuracy in predicting users' decisions whether or not to allow the respective IoT scenario.

关键词： IoT privacy preference modeling experience sampling Google Glass K-modes clustering conditional inference tree

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preference-Aware Human Spatial Behavior modeling in Cyber-Physical-Human Systems 5

Preference-Aware Human Spatial Behavior Modeling in Cyber-Ph...

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5th IFAC Workshop on Cyber-Physical Human Systems

作者： Doctorarastoo, Maral Flanigan, Katherine A. Berges, Mario Carnegie Mellon Univ Dept Civil & Environm Engn 5000 Forbes Ave Pittsburgh PA 15213 USA

This study introduces a new approach for modeling preference-aware human spatial behavior in cyber-physical-human systems using Graph Neural Networks (GNN) and Reinforcement Learning (RL). Current models often overlook the causality and impact of factors influencing preferences. Our approach utilizes GNN for its advanced handling of spatial data, capturing physical, social, and environmental features and their human perception. Integrated with RL, the model dynamically adapts to changes in the surrounding environment. We illustrate the approach in an educational conference room setting, comparing student behavior simulations with and without preferences. The results indicate that preference incorporation leads to significantly more realistic simulations. Copyright (c) 2024 The Authors.

关键词： Agent-based modeling cyber-physical-human systems human behavior modeling human-infrastructure interaction preference modeling

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preference Enhanced Social Influence modeling for Network-Aware Cascade Prediction 22

Preference Enhanced Social Influence Modeling for Network-Aw...

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45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Wu, Likang Wang, Hao Chen, Enhong Li, Zhi Zhao, Hongke Ma, Jianhui Univ Sci & Technol China Sch Comp Sci & Technol Anhui Prov Key Lab Big Data Anal & Applicat Hefei Anhui Peoples R China State Key Lab Cognit Intelligence Hefei Anhui Peoples R China Tianjin Univ Coll Management & Econ Tianjin Peoples R China

ISBN: (纸本)9781450387323

Network-aware cascade size prediction aims to predict the final reposted number of user-generated information via modeling the propagation process in social networks. Estimating the user's reposting probability by social influence, namely state activation plays an important role in the information diffusion process. Therefore, Graph Neural Networks (GNN), which can simulate the information interaction between nodes, has been proved as an effective scheme to handle this prediction task. However, existing studies including GNN-based models usually neglect a vital factor of user's preference which influences the state activation deeply. To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i.e., preference topics generation, preference shift modeling, and social influence activation. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate. Extensive experiments on two large-scale real-world datasets have clearly demonstrated the effectiveness of our proposed model compared to state-of-the-art baselines.

关键词： cascade prediction graph neural networks preference modeling text semantics network-aware diffusion

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Pacos: modeling preference reversals in users' context-dependent choices

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KNOWLEDGE-BASED SYSTEMS 2023年第1期278卷

作者： Li, Qingming Zhao, Vicky Zhejiang Lab AI Res Inst Hangzhou 311100 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

Choice problems refer to the problem of selecting the best choices from several available items, and learning users' preferences in choice problems is of great importance in understanding users' decision making mechanisms and providing personalized services. Existing works typically assume that people evaluate items independently. In practice, however, users' preferences depend on the market in which items are placed, which is known as the context effects;and the order of users' preferences for two items may even be reversed, which is called preference reversals. In this work, we identify three factors contributing to the context effects: users' adaptive weights, the inter-item comparison, and display positions. We propose a context-dependent preference model named Pacos as a unified framework to address three factors simultaneously, and consider two design methods including an additive method with high interpretability and an ANN-based method with high accuracy. We study the conditions for preference reversals to occur and provide a theoretical proof of the effectiveness of Pacos in predicting when preference reversals would occur. Experimental results show that the proposed method has better performance than prior works in predicting users' choices, and has great interpretability to help understand the cause of preference reversals. (c) 2023 Elsevier B.V. All rights reserved.

关键词： preference reversals Context effects Choice problems preference modeling

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Competition-driven robust multicriteria ranking for managing interactive generalized orthopair information in humanitarian operations

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INFORMATION SCIENCES 2025年 700卷

作者： Zhang, Wenkai Gao, Hengxia Guo, Haixiang Pamucar, Dragan China Univ Geosci Sch Econ & Management Beijing 100083 Peoples R China Beijing Wuzi Univ Sch Logist Beijing 101149 Peoples R China China Univ Geosci Sch Econ & Management Wuhan 430078 Peoples R China Vilnius Gediminas Tech Univ Transport & Logist Competence Ctr Vilnius Lithuania

Multicriteria decision-making (MCDM) provides a structured framework to support decision- making in humanitarian organizations, particularly by considering the evaluations and preferences of decision makers in the form of generalized orthopair fuzzy sets. To address the challenges posed by interactive generalized orthopair MCDM problems involving competition and higher- order uncertainty, a competition-driven robust multicriteria ranking approach based on a three-stage solution framework is proposed. Firstly, two kinds of game cross-closeness models based on benevolent and aggressive competition strategies are developed to determine the optimal capacities characterizing interactions among criteria. Building upon this, we propose a competition-driven Choquet decision method that incorporates a set of novel operational laws for generalized orthopair fuzzy sets, which effectively addresses potential authoritarian behavior. Subsequently, an interactive robust ranking approach, grounded in stochastic multicriteria acceptability analysis, is presented to address the simultaneous uncertainties inherent in decision models and model inputs, allowing for competition among alternatives. Finally, the effectiveness and advantages of the proposed robust multicriteria ranking approach are demonstrated through a case study, a series of experiments, and a comprehensive comparative analysis. The results demonstrate that the proposed approach not only provides robust recommendations but also excels in effectively managing competing alternatives while reducing cognitive burden on decision makers.

关键词： Multicriteria decision-making Q-rung orthopair fuzzy sets Robust ranking Choquet integral preference modeling

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Identical Human preference Alignment Paradigm for Text-to-Image Models

Identical Human Preference Alignment Paradigm for Text-to-Im...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sun, Haoyuan Xia, Bo Zhao, Yifei Chang, Yongzhe Wang, Xueqian Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798350368741

Implicit reward mechanism of Direct preference Optimization (DPO) has facilitated its recent applications beyond large language models (LLMs), notably in aligning text-to-image models with human preferences. While promising results have been achieved with algorithms such as Diffusion-DPO, their reliance on the assumptions of the Bradley-Terry model could potentially lead to significant overfitting. In this paper, we propose the Step Identical preference Alignment (SIPA) method, departing text-to-image alignment from the assumptions of Bradley-Terry preference model. We assess the performance of four models, Diffusion-DPO, SPO, and SIPA, alongside the original model, on the HPS-V2 test set, which focus on three key aspects: text-image alignment, human value alignment, and generation diversity. Experimental results show that SIPA matches or outperforms existing SOTA alignment methods, and even exceeds the original model in terms of generation diversity, which compellingly demonstrates SIPA's superiority in mitigating alignment overfitting. © 2025 IEEE.

关键词： human preference alignment preference modeling text-to-image model

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Fifty years of multiple criteria decision analysis: From classical methods to robust ordinal regression

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第2期323卷 351-377页

作者： Greco, Salvatore Slowinski, Roman Wallenius, Jyrki Univ Catania Dept Econ & Business I-95129 Catania Italy Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Aalto Univ Sch Business POB 11000 Aalto 00076 Finland

Multiple Criteria Decision Analysis (MCDA) is a subfield of Operational Research that aims to support Decision- Makers (DMs) in the decision-making process through mathematical models and computational procedures. In this perspective, MCDA employs structured and traceable protocols to identify potential actions and the criteria for evaluating them. MCDA procedures aim to define recommendations consistent with the preferences of DMs for the specific decision problem at hand. These problems are generally formulated in terms of either choosing the best action, classifying actions into pre-defined and ordered decision classes, or ranking actions from best to worst. As the evaluation criteria are generally conflicting, the main challenge is to aggregate them into a mathematical preference model representing the DM value system. We review the development of MCDA over the past fifty years and describe its evolution with examples of distinctive methods. They are distinguished by the type of preference information elicited by DMs, the type of the preference model (criteria aggregation), and the way of converting the preference relation induced by the preference model in the set of potential actions into a decision recommendation. We focus on MCDA methods with a finite set of actions. References to specific application areas will be given. In the conclusion section, some prospective avenues of research will be outlined.

关键词： Multiple criteria decision analysis preference modeling Best choice Ordinal classification Ranking

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Probe: Learning Users' Personal Projection Bias in Inter-Temporal Choices

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 928-941页

作者： Li, Qingming Zhao, H. Vicky Zhejiang Lab Res Inst Artificial Intelligence Hangzhou 311100 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

Inter-temporal choices involve making decisions that require weighing costs in the present against benefits in the future. One specific type of inter-temporal choice is the decision between purchasing an individual item at full price or opting for a bundle including that item at a discounted price. Previous works assume that users have accurate expectations of factors involved in these decisions. However, in reality, users' perceptions of these factors are often biased, leading to irrational and suboptimal decision-making. In this work, we focus on two commonly observed biases: the projection bias and the reference-point effect. To address these biases, we propose a novel bias-embedded preference model called Probe. Probe introduces prospect theory from behavioral economics to model these biases, which incorporates a weight function to capture the projection bias and a value function to account for the reference-point effect. We propose a new method to learn users' individual projection biases from historical records and present four methods to estimate the reference point within the value function. We theoretically analyze the impact of projection bias on bundle pricing strategies. Through experimental results, we show that the proposed Probe model outperforms existing methods and leads to a better understanding of users' behaviors in bundle purchases. This investigation can facilitate a deeper comprehension of users' decision-making processes, enable personalized services, and help sellers design better product bundling strategies.

关键词： Behavioral sciences Decision making Pricing Probes Games Costs Context modeling Projection bias reference-point effect bundle purchases choice problems preference modeling

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RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework With LLM Agents

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第5期11卷 6759-6770页

作者： Shu, Yubo Zhang, Haonan Gu, Hansu Zhang, Peng Lu, Tun Li, Dongsheng Gu, Ning Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Shanghai Key Lab Data Sci Fudan Inst Aging MOELab Natl Dev & Intelligent Governance Shanghai 200433 Peoples R China Fudan Univ Shanghai Inst Intelligent Elect & Syst Shanghai 200433 Peoples R China Microsoft Res Asia Shanghai 200241 Peoples R China

The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in human-computer interaction (HCI) by tailoring content based on individual preferences. Despite their importance, challenges persist in balancing recommendation accuracy with user satisfaction, addressing biases while preserving user privacy, and solving cold-start problems in cross-domain situations. This research argues that addressing these issues is not solely the recommender systems' responsibility, and a human-centered approach is vital. We introduce the recommender system, assistant, and human (RAH) framework, an innovative solution with large language model (LLM)-based agents such as perceive, learn, act, critic, and reflect, emphasizing the alignment with user personalities. The framework utilizes the learn-act-critic loop and a reflection mechanism for improving user alignment. Using the real-world data, our experiments demonstrate the RAH framework's efficacy in various recommendation domains, from reducing human burden to mitigating biases and enhancing user control. Notably, our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.

关键词： Human-centered recommendation mitigate bias preference modeling reduce user burden user control

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Inferring Individual Human Mobility From Sparse Check-in Data: A Temporal-Context-Aware Approach

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第1期11卷 600-611页

作者： Xu, Shuai Fu, Xiaoming Pi, Dechang Ma, Zhuo Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Univ Gottingen Inst Comp Sci D-37077 Gottingen Germany Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Jiangsu Police Inst Dept Comp Informat & Cyber Secur Nanjing 210031 Peoples R China

Inferring individual human mobility at a given time is not only beneficial for personalized location-based services but also crucial for tracking trajectory of the confirmed cases in the COVID-19 pandemic. However, individual-generated trajectory data from mobile Apps are characterized by implicit feedback, which means only a few individual-location interactions can be observed. Existing studies based on such sparse trajectory data are not sufficient to infer an individual's missing mobility in his/her historical trajectory and further predict an individual's future mobility at a given time under a unified framework. To address this concern, in this article, we propose a temporal-context-aware framework that incorporates multiple factors to model the time-sensitive individual-location interactions in a bottom-up way. Based on the idea of feature fusion, the driving effect of heterogeneous information on an individual's mobility is gradually strengthened, so that the temporal-spatial context when a check-in occurs can be accurately perceived. We leverage Bayesian personalized ranking (BPR) to optimize the model, where a novel negative sampling method is employed to alleviate data sparseness. Based on three real-world datasets, we evaluate the proposed approach with regard to two different tasks, namely, missing mobility inference and future mobility prediction at a given time. Experimental results encouragingly demonstrate that our approach outperforms multiple baselines in terms of two evaluation metrics. Furthermore, the predictability of individual mobility within different time windows is also revealed.

关键词： Context perception feature fusion individual human mobility location prediction preference modeling

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