Relevant bibliographies by topics / User Preference Models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'User Preference Models'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "User Preference Models"

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Luo, Mingshi, Xiaoli Zhang, Jiao Li, Peipei Duan, and Shengnan Lu. "User Dynamic Preference Construction Method Based on Behavior Sequence." Scientific Programming 2022 (July 22, 2022): 1–15. http://dx.doi.org/10.1155/2022/6101045.

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People’s needs are constantly changing, and the performance of traditional recommendation algorithms is no longer enough to meet the demand. Considering that users’ preferences change with time, the users’ behavior sequence hides the evolution and change law of users’ preferences, so mining the dependence of the users’ behavior sequence is extremely important to predict users’ dynamic preferences. From the perspective of constructing users’ dynamic preferences, this paper proposes a users’ dynamic preference model based on users’ behavior sequences. Firstly, the user’s interest model is divide
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Savia, Eerika, Kai Puolamäki, and Samuel Kaski. "Latent grouping models for user preference prediction." Machine Learning 74, no. 1 (2008): 75–109. http://dx.doi.org/10.1007/s10994-008-5081-7.

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Yang, Taoru, Yong Gao, Zhou Huang, and Yu Liu. "UPTDNet: A User Preference Transfer and Drift Network for Cross-City Next POI Recommendation." International Journal of Intelligent Systems 2023 (March 4, 2023): 1–17. http://dx.doi.org/10.1155/2023/9091570.

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Cross-city point of interest (POI) recommendation for tourists in an unfamiliar city has high application value but is challenging due to the data sparsity. Most existing models attempt to alleviate the sparsity problem by learning the user preference transfer and drift. However, they either fail to simultaneously model the preference transfer and drift in both long- and short-term user preferences or cannot accomplish the task of the next POI recommendation, which is crucial for a wide spectrum of applications ranging from transportation and urban planning to advertising. To address the limit
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Wu, Weidong, Xiaoyan Sun, Guangyi Man, Shuai Li, and Lin Bao. "Interactive Multifactorial Evolutionary Optimization Algorithm with Multidimensional Preference Surrogate Models for Personalized Recommendation." Applied Sciences 13, no. 4 (2023): 2243. http://dx.doi.org/10.3390/app13042243.

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Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USMbased IEAs have not investigated how knowledge migrates between preference models to improve the diversity and novelty of recommendations. Motivated by this, an interactive multifactorial evolutionary optimization algorithm with multidimensional preference user surrogate models is proposed here to perform a multi-view o
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Zhang, Wumei, Jianping Zhang, and Yongzhen Zhang. "A Comment Aspect-Level User Preference Transfer Model for Cross-Domain Recommendations." Information Resources Management Journal 37, no. 1 (2024): 1–25. http://dx.doi.org/10.4018/irmj.345360.

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Traditional cross-domain recommendation models make it difficult to deeply mine users' aspect-level preferences from comment information due to existing problems such as polysemy of comment text, sparse comment data, and user cold start. A Cross-Domain Recommender (CDR) model that integrates comment knowledge enhancement and aspect-level user preference transfer (C-KE-AUT) was proposed to address the above issues. Firstly, an aspect-level user preference extraction model was constructed by combining the RoBERTa word embedding model, high-level feature representation based on Transformer, and a
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Aliev, A., and Z. Maharramov. "FEATURES OF BUILDING MODELS OF USER PREFERENCES FOR CLOUD SERVICES AND THEIR CLASSIFICATION." Sciences of Europe, no. 113 (March 27, 2023): 82–85. https://doi.org/10.5281/zenodo.7773850.

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User preference models are critical in understanding how users interact with cloud services. By studying factors such as cost, performance, security, ease of use, and reliability, these models offer insights into what motivates users to adopt cloud services and what factors may prevent them from doing so. Cloud service providers can use these models to deliver personalized and relevant content, optimize service delivery, and ensure user privacy and security. In the article, on the example of an airline, experimental data are collected and their classification is considered using the confusion
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Zhou, Yinglian, and Jifeng Chen. "Time Series Geographic Social Network Dynamic Preference Group Query." International Journal of Information Systems in the Service Sector 13, no. 4 (2021): 18–39. http://dx.doi.org/10.4018/ijisss.2021100102.

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Driven by experience and social impact of the new life, user preferences continue to change over time. In order to make up for the shortcomings of existing geographic social network models that often cannot obtain user dynamic preferences, a time-series geographic social network model was constructed to detect user dynamic preferences, a dynamic preference value model was built for user dynamic preference evaluation, and a dynamic preferences group query (DPG) was proposed in this paper . In order to optimize the efficiency of the DPG query algorithm, the UTC-tree index user timing check-in re
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Ye, Yuyang, Zhi Zheng, Yishan Shen, et al. "Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13069–77. https://doi.org/10.1609/aaai.v39i12.33426.

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Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs). Most existing studies have focused on converting user behavior logs into textual prompts and leveraging techniques such as prompt tuning to enable LLMs for recommendation tasks. Meanwhile, research interest has recently grown in multimodal recommendation systems that integrate data from images, text, and other sources using modality fusion techniques. This introduces new challenges to the existing LLM-based recommendation paradigm which relies solely on text mod
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Rivera-Abarca, Ana Lucía, Jazmín Isabel García-Guerra, Héctor Oswaldo Aguilar-Cajas, Heidy Elizabeth Vergara-Zurita, José Israel López-Pumalema, and Freddy Armijos-Arcos. "Predictive Models of Typographic Preference in Digital Media." Data and Metadata 4 (June 3, 2025): 1062. https://doi.org/10.56294/dm20251062.

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Introduction: This article explores how typography influences user experience in digital environments, highlighting its evolution from the 11th century to the Internet era. Objective: The aim of this research was to examine the psychological impact of fonts, which evoke emotional responses and affect readability, design and user behavior. Methodology: Predictive models, such as regression, classification and time series, are used to analyze typographic preferences, helping designers to optimize digital interfaces. Results: The study simulated data from 1,000 participants, considering variables
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Gautschi, David A., and Darius J. Sabavala. "Incorporating user costs in preference models for service alternatives." Marketing Letters 2, no. 3 (1991): 281–91. http://dx.doi.org/10.1007/bf02404078.

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Dissertations / Theses on the topic "User Preference Models"

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Valencia, Rodríguez Salvador. "Location Aware Multi-criteria Recommender System for Intelligent Data Mining." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23418.

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One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the m
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Nadee, Wanvimol. "Modelling user profiles for recommender systems." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93723/1/Wanvimol_Nadee_Thesis.pdf.

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Recommender systems assist users in finding what they want. The challenging issue is how to efficiently acquire user preferences or user information needs for building personalized recommender systems. This research explores the acquisition of user preferences using data taxonomy information to enhance personalized recommendations for alleviating cold-start problem. A concept hierarchy model is proposed, which provides a two-dimensional hierarchy for acquiring user preferences. The language model is also extended for the proposed hierarchy in order to generate an effective recommender algorith
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Aarts, Geert. "Modelling space-use and habitat preference from wildlife telemetry data." Thesis, St Andrews, 2007. http://hdl.handle.net/10023/327.

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Tsai, Geoffrey T. "The tools we use : a study of user preferences for sketches, prototypes, and CAD models and the influence on design outcome." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106785.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages [58]-[60]).<br>During a product design and development process, design teams use a variety of tools to generate and represent multiple design options before they eventually arrive at a singular design solution. Studying how these tools can influence the design outcome has the potential to enable designers to become more aware of the choices they make when they choose to use a tool. Because so much of the cost o
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Algrnas, Mohammad. "Stakeholder model representing consumer preferences for housing in Saudi Arabia." Thesis, Liverpool John Moores University, 2016. http://researchonline.ljmu.ac.uk/4479/.

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Adequate housing is an important issue for any society; no one can ignore the importance of providing adequate housing for citizens in any country. Purchasing a home is a major investment; it takes a huge part of people’s income. Therefore, it is important to know what in the market is suitable for consumers, to categorise and analyse consumer preferences, and understand their changes in behaviour, looking at the differences in the demographics and the population segment in order to create a better home environment. However, solutions that are not consistent with consumer ambitions and self-co
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Recalde, Lorena. "Modeling users preferences in online social networks." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663756.

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L'objectiu d'aquesta tesi és desenvolupar nous i diversos mètodes per modelar les preferències dels usuaris a les Xarxes Socials Online. Els mètodes proposats tenen com a finalitat ser aplicats en àrees de recerca com la Personalització o Recomanació d'ítems i la Detecció de Grups d'Usuaris amb gustos similars. Aquests mètodes poden ser agrupats en dos tipus: i) mètodes basats en tècniques d'anàlisi de textos (Part I, Capítols del 3 al 5) i ii) mètodes basats en teoria de grafs (Part II, Capítols 6 i 7). Amb els mètodes plantejats a la Part I és possible determinar el nivell d'interès dels
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Johansson, Anton, and Christoffer Sjöholm. "User Preferences of Application Attributes During Product Browsing : An Investigation of Customer Experience in Fashion E-Commerce." Thesis, Linköpings universitet, Logistik- och kvalitetsutveckling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158673.

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In a fast-changing retail environment, including hard competition and demanding consumers, the customer experience of the purchasing service is crucial to gain a competitive advantage. Since consumers are to some extent moving from offline to online, and from desktop shopping to purchasing clothing in a mobile application, there is a need for investigating consumers expectations of their experience of a mobile application. The existing and performance of attributes and functions determines the satisfaction of the user experience, which is why it is reasonable to investigate expectations concer
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Noor, Ifada. "Personalized ranking for tag-based item recommendation system using tensor model." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/98426/1/_Noor%20Ifada_Thesis.pdf.

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This research is a step forward in the study of generating item recommendations for the tag-based systems, in which two efficient tagging data interpretation schemes and four ranking methods are developed. The interpretation schemes apply ranking constraints to interpret the tagging data that allow a ranked representation and result in richer data. The ranking methods fall into the category of point-wise and list-wise based ranking approaches that consider the recommendation task as regression/classification and ranking respectively. This thesis, in particular, shows that tagging data interpre
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Liu, Yulin. "Urban transit quality of service : user perception and behaviour." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/61517/1/Yulin_Liu_Thesis.pdf.

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Despite its potential multiple contributions to sustainable policy objectives, urban transit is generally not widely used by the public in terms of its market share compared to that of automobiles, particularly in affluent societies with low-density urban forms like Australia. Transit service providers need to attract more people to transit by improving transit quality of service. The key to cost-effective transit service improvements lies in accurate evaluation of policy proposals by taking into account their impacts on transit users. If transit providers knew what is more or less important t
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Shifula, Loide Ndahafa. "The contexts which Namibian learners in grades 8 to 10 prefer to use in mathematics." Thesis, University of the Western Cape, 2012. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_3650_1383744876.

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<p>One of the key ideas in the research on mathematics education is that the mathematical knowledge that learners acquire is strongly tied to the particular situation in which it is learnt. This study investigated the contexts that learners in grades eight, nine and ten prefer to deal with in the learning of mathematics based on their personal, social, societal, cultural and contextual concerns or affinities. The study is situated in the large-scale project called the Relevance of School Mathematics Education II (ROSMEII), which is concerned with the application and the use of mathematical kno
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Books on the topic "User Preference Models"

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Daniłowicz, Czesław. Modele systemów wyszukiwania informacji uwzględniające preferencje użytkowników końcowych. Wydawn. Politechniki Wrocławskiej, 1992.

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Brodeckiy, Gennadiy, Denis Gusev, Viktoriya Gerami, Ol'ga Sviridova, and Ivan Shidlovskiy. Multi-criteria optimization in supply chains. INFRA-M Academic Publishing LLC., 2024. https://doi.org/10.12737/2127019.

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The textbook contains current types of problems of choosing the best solutions for many criteria used in modeling supply chains. To solve the problems, methods and models are presented, including traditional and special approaches to multi-criteria optimization of solutions. Special attention is paid to additional opportunities for improving such approaches to improve decision optimization procedures in supply chain modeling, and the possibilities of alternative filtering procedures are considered. Special procedures are presented to eliminate the phenomena of inadequate choice and effectively
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Banovic, Nikola, Jennifer Mankoff, and Anind K. Dey. Computational Model of Human Routine Behaviours. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0015.

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Computational Interaction enables a future in which user interfaces (UI) learn about people’s behaviours by observing them and interacting with them to help people to be productive, comfortable, healthy, and safe. However, this requires technology that can accurately model people’s behaviours. This chapter focuses on human routine behaviours enacted by people as sequences of actions performed in specific situations, i.e. behaviour instances, and presents a probabilistic, generative model of human routine behaviours that can describe, reason about, and act in response to people’s behaviours. We
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Yee, Nick, and Nicolas Ducheneaut. Gamer motivation profiling: uses and applications. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794844.003.0028.

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Gamers are not a monolithic group; gaming preferences and motivations vary in important ways among gamers. An empirical, validated model of gaming motivations provides a crucial methodological bridge between player preferences and their in-game behaviours, and, more importantly, engagement and retention outcomes. Instead of simply seeing on a key performance indicator dashboard that a certain percentage of gamers are leaving, a motivation model allows us to pinpoint why those gamers are leaving.
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Laver, Michael, and Ernest Sergenti. Party Leaders with Policy Preferences. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0010.

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This chapter adapts the dynamic model of multiparty competition to take into account the possibility that party leaders take their own preferences into account when they set party policy. If they do this, they must make trade-offs between satisfying their private policy preferences and some other objective, whether this is maximizing party vote share or pleasing current party supporters. Models that specify such trade-offs have often been found intractable using traditional analytical techniques. However, they are straightforward to specify and analyze using computational agent-based modeling,
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Abellan, Jose Maria, Carmen Herrero, and Jose Luis Pinto. QALY-Based Cost-Effectiveness Analysis. Edited by Matthew D. Adler and Marc Fleurbaey. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199325818.013.8.

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This chapter introduces the main ideas about the use of quality-adjusted life years (QALYs) in the evaluation of health policies. It starts by explaining the theoretical underpinnings of the QALY model understood as individual utilities. Afterward, it reviews the empirical evidence about the descriptive validity of the main assumptions supporting the model. Then, it explains the main preference elicitation techniques (visual analog scale, time trade-off, and standard gamble). It also shows the practical psychological problems faced by these techniques, such as the existence of context-dependen
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Miller, Nicholas R. Social Choice Theory and Legislative Institutions. Oxford University Press, 2016. http://dx.doi.org/10.1093/acrefore/9780190228637.013.1.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Politics. Please check back later for the full article.Narrowly understood, social choice theory is a specialized branch of applied logic and mathematics that analyzes abstract objects called preference aggregation functions, social welfare functions, and social choice functions. But more broadly, social choice theory identifies, analyzes, and evaluates rules that may be used to make collective decisions. So understood, social choice is a subfield of the social sciences that examines what may be called “
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Boland, Lawrence A. Building models of non-clearing markets. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190274320.003.0015.

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This chapter examines the extent to which Keynesian models can overcome the limits of equilibrium models without violating the methodological individualism that is required in all neoclassical equilibrium models. This chapter discusses an approach that involves a generalized version of Keynesian liquidity preference due to John Hicks. It goes beyond financial liquidity by recognizing the possible desirability of deliberate excess capacity. The generalized version involves endogenously deliberate disequilibria during which participants with incomplete knowledge of the market’s future are unders
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Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.

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This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that a
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Laver, Michael, and Ernest Sergenti. Party Competition. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.001.0001.

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Party competition for votes in free and fair elections involves complex interactions by multiple actors in political landscapes that are continuously evolving, yet classical theoretical approaches to the subject leave many important questions unanswered. This book offers the first comprehensive treatment of party competition using the computational techniques of agent-based modeling. This exciting new technology enables researchers to model competition between several different political parties for the support of voters with widely varying preferences on many different issues. The book models
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Book chapters on the topic "User Preference Models"

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Linden, Greg, Steve Hanks, and Neal Lesh. "Interactive Assessment of User Preference Models: The Automated Travel Assistant." In User Modeling. Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-2670-7_9.

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Schmitt, Christian, Dietmar Dengler, and Mathias Bauer. "Multivariate Preference Models and Decision Making with the MAUT Machine." In User Modeling 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44963-9_40.

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Eno, Joshua, Gregory Stafford, Susan Gauch, and Craig W. Thompson. "Hybrid User Preference Models for Second Life and OpenSimulator Virtual Worlds." In User Modeling, Adaption and Personalization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22362-4_8.

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Nahal, Yasmine, Markus Heinonen, Mikhail Kabeshov, et al. "Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72381-0_6.

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AbstractIn recent years, there has been growing interest in leveraging human preferences for drug discovery to build models that capture chemists’ intuition for de novo molecular design, lead optimization, and prioritization for experimental validation. However, existing models derived from human preferences in chemistry are often black-boxes, lacking interpretability regarding how humans form their preferences. Enhancing transparency in human-in-the-loop learning is crucial to ensure that such approaches in drug discovery are not unduly affected by subjective bias, noise or inconsistency. Mor
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Zymla, Mark-Matthias, Raphael Buchmüller, Miriam Butt, and Daniel Keim. "Deciphering Personal Argument Styles – A Comprehensive Approach to Analyzing Linguistic Properties of Argument Preferences." In Robust Argumentation Machines. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63536-6_18.

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AbstractIn this paper, we introduce an application for exploring the effect of linguistic features on personalized argument preferences. These individual preferences are derived by measuring the impact of linguistic features on pairwise comparisons between arguments. The insights derived from this are, in turn, useful for studies of argument quality. To conduct this research, we have developed a new pipeline that covers three major components: data collection, argument comparison labeling, and data exploration, incorporating linguistic annotations of arguments and preference data. The first co
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Wang, Tianxiong, Liu Yang, Xian Gao, and Yuxuan Jin. "A Comparative Research on Designer and Customer Emotional Preference Models of New Product Development." In Design, User Experience, and Usability. Interaction Design. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49713-2_39.

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Valencia Rodríguez, Salvador, and Herna Lydia Viktor. "A Personalized Location Aware Multi-Criteria Recommender System Based on Context-Aware User Preference Models." In IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41142-7_4.

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Arroyo Chavez, Mariana, Bernard Thompson, Molly Feanny, et al. "Customization of Closed Captions via Large Language Models." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62849-8_7.

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AbstractThis study investigates the feasibility of employing artificial intelligence and large language models (LLMs) to customize closed captions/subtitles to match the personal needs of deaf and hard of hearing viewers. Drawing on recorded live TV samples, it compares user ratings of caption quality, speed, and understandability across five experimental conditions: unaltered verbatim captions, slowed-down verbatim captions, moderately and heavily edited captions via ChatGPT, and lightly edited captions by an LLM optimized for TV content by AppTek, LLC. Results across 16 deaf and hard of hear
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Simari, Gerardo I., Cristian Molinaro, Maria Vanina Martinez, Thomas Lukasiewicz, and Livia Predoiu. "Models for Representing User Preferences." In Ontology-Based Data Access Leveraging Subjective Reports. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65229-0_2.

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Takama, Yasufumi, and Yuki Muto. "Mining User Preference Model from Utterances." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01091-0_5.

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Conference papers on the topic "User Preference Models"

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Gould, Adam, Guilherme Paulino-Passos, Seema Dadhania, Matthew Williams, and Francesca Toni. "Preference-Based Abstract Argumentation for Case-Based Reasoning." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/37.

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In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifically, we introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We prove that the model inherently follows these preferences when making predictions and show th
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Papadimitriou, Dimitris, and Daniel S. Brown. "Bayesian Constraint Inference from User Demonstrations Based on Margin-Respecting Preference Models." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611095.

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Saraf, Vinod, and Vinod Pachghare. "User Preference Based Cumulative Trust (UPBCT) Computation Model." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985159.

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Saraf, Vinod, and Vinod Pachghare. "User Preference Based Cumulative Trust (UPBCT) Computation Model." In 2025 8th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech). IEEE, 2025. https://doi.org/10.1109/iementech65115.2025.10959448.

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Caccavale, Fiammetta, Carina L. Gargalo, Krist V. Gernaey, Ulrich Kr�hne, and Alessandra Russo. "Beyond ChatGMP: Improving LLM generation through user preferences." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.144855.

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Prompt engineering � improving the command given to a large language model (LLM) � is becoming increasingly useful in order to maximize the performance of the model and therefore the quality of the output. However, in certain instances, the user is not able to enrich the prompt with additional and personalized details, such as the preferred tone and length of generated response. Therefore, it is useful to create models that learn these preferences and implement them directly in the prompt. Current state-of-the-art inductive logic programming (ILP) systems can play an important role in the deve
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Yu, Jianbing, and Guodong Ma. "Ontology semantic information retrieval model based on user preference." In 2025 IEEE 7th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2025. https://doi.org/10.1109/cisce65916.2025.11065896.

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"ITEM-USER PREFERENCE MAPPING WITH MIXTURE MODELS - Data Visualization for Item Preference." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002274001050111.

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Martin, Carlos, Craig Boutilier, Ofer Meshi, and Tuomas Sandholm. "Model-Free Preference Elicitation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/387.

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In recommender systems, preference elicitation (PE) is an effective way to learn about a user's preferences to improve recommendation quality. Expected value of information (EVOI), a Bayesian technique that computes expected gain in user utility, has proven to be effective in selecting useful PE queries. Most EVOI methods use probabilistic models of user preferences and query responses to compute posterior utilities. By contrast, we develop model-free variants of EVOI that rely on function approximation to obviate the need for specific modeling assumptions. Specifically, we learn user response
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Qi, Lianyong, Yuwen Liu, Weiming Liu, et al. "Counterfactual User Sequence Synthesis Augmented with Continuous Time Dynamic Preference Modeling for Sequential POI Recommendation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/255.

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With the proliferation of Location-based Social Networks (LBSNs), user check-in data at Points-of-Interest (POIs) has surged, offering rich insights into user preferences. However, sequential POI recommendation systems always face two pivotal challenges. A challenge lies in the difficulty of modeling time in a discrete space, which fails to accurately capture the dynamic nature of user preferences. Another challenge is the inherent sparsity and noise in continuous POI recommendation, which hinder the recommendation process. To address these challenges, we propose counterfactual user sequence s
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Leahu, Haralambie, Michael Kaisers, and Tim Baarslag. "Automated Negotiation with Gaussian Process-based Utility Models." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/60.

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Designing agents that can efficiently learn and integrate user's preferences into decision making processes is a key challenge in automated negotiation. While accurate knowledge of user preferences is highly desirable, eliciting the necessary information might be rather costly, since frequent user interactions may cause inconvenience. Therefore, efficient elicitation strategies (minimizing elicitation costs) for inferring relevant information are critical. We introduce a stochastic, inverse-ranking utility model compatible with the Gaussian Process preference learning framework and integrate i
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Reports on the topic "User Preference Models"

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Apesteguia, Jose, Miguel A. Ballester, and Ángelo Gutiérrez-Daza. Random Discounted Expected Utilit. Banco de México, 2024. http://dx.doi.org/10.36095/banxico/di.2024.03.

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This paper introduces the random discounted expected utility (RDEU) model, which we have developed as a means to deal with heterogeneous risk and time preferences. The RDEU model provides an explicit linkage between preference and choice heterogeneity. We prove it has solid comparative statics, discuss its identification, and demonstrate its computational convenience. Finally, we use two distinct experimental datasets to illustrate the advantages of the RDEU model over common alternatives for estimating heterogeneity in preferences across individuals.
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Dixon, Peter, Michael Jerie, and Maureen Rimmer. Modern Trade Theory for CGE Modelling: the Armington, Krugman and Melitz Models. GTAP Technical Paper, 2015. http://dx.doi.org/10.21642/gtap.tp36.

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This paper is for CGE modelers and others interested in modern trade theory. The Armington specification of trade, assuming country-level product differentiation, has been central to CGE modelling for 40 years. Starting in the 1980s with Krugman and more recently Melitz, trade theorists have preferred specifications with firm-level product differentiation. We draw out the connections between the Armington, Krugman and Melitz models, deriving them as successively less restrictive special cases of an encompassing model. We then investigate optimality properties of the Melitz model, demonstrating
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Aguiar, Angel, Caitlyn Carrico, Thomas Hertel, Zekarias Hussein, Robert McDougall, and Badri Narayanan. Extending the GTAP framework for public procurement analysis. GTAP Working Paper, 2016. http://dx.doi.org/10.21642/gtap.wp82.

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This paper extends the GTAP framework to aid in the analysis of changes to public procurement policies. In terms of data developments, government investment demand data is estimated for each of the 57 GTAP Commodities in the 140 regions of version 9. In addition, the origin of imports by end use (i.e., for firms, private consumption, government consumption, and investment) is determined following the recent literature. Another layer of valuation is also introduced, which captures the preferences towards domestic production. In terms of model extensions, there is a new nest in the production st
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Carroll, Daniel R., André Victor D. Luduvice, and Eric R. Young. A Note on Aggregating Preferences for Redistribution. Federal Reserve Bank of Cleveland, 2024. http://dx.doi.org/10.26509/frbc-wp-202427.

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The policy predictions of standard heterogeneous agent macroeconomic models are often at odds with observed policies. We use the 2021 General Social Survey to investigate the drivers of individuals' preferences over taxes and redistribution. We find that these preferences are more strongly associated with political identity than with economic status. We discuss the implications for quantitative macroeconomic models with endogenous policy determination.
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Oviedo, Daniel, Yisseth Scorcia, and Lynn Scholl. Ride-hailing and (dis)Advantage: Perspectives from Users and Non-users. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003656.

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The introduction of ride-hailing in cities of Latin America and the Caribbean (LAC) remains a relatively new topic in regional research and a contentious issue in local policy and practice. Evidence regarding users and how do they differ from non-users is scarce, and there is little documented evidence about how user preferences and perceptions may influence the uptake of ride-hailing. This paper uses primary data from a survey collected from users and non-users of ride-hailing in Bogotá during 2019 to develop a Latent Class Analysis Model (LCA) to identify clusters of users and non-users of r
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Chong, Alberto E., and Mark Gradstein. Education and Democratic Preferences. Inter-American Development Bank, 2009. http://dx.doi.org/10.18235/0010914.

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This paper examines the causal link between education and democracy. Motivated by a model whereby educated individuals are in a better position to assess the effects of public policies and hence favor democracy where their opinions matter, the empirical analysis uses World Values Surveys to study the link between education and democratic attitudes. Controlling for a variety of characteristics, the paper finds that higher education levels tend to result in rodemocracy views. These results hold across countries with different levels of democracy, thus rejecting the hypothesis that indoctrination
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Agrawal, Asha Weinstein, and Hilary Nixon. Investing in California’s Transportation Future: 2022 Public Opinion on Critical Needs. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2158.

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This study surveyed 3,821 adults living in California about their general travel behaviors and resources, use of ride-hailing, performance ratings for the transportation system and agencies responsible for transportation, transportation system improvement priorities, and preference for how transportation funds are allocated. Key findings include the following: • Californians are multi-modal: Although driving was the most common mode, respondents reported that in the previous 30 days 66% had made a walk trip, 28% had used ridehailing, 25% had used public transit, and 22% had bicycled. • Althoug
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Albrecht, Brian C., Thomas M. Phelan, and Nick Pretnar. Time Use and the Efficiency of Heterogeneous Markups. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202328.

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What are the welfare implications of markup heterogeneity across firms? In standard monopolistic competition models, such heterogeneity implies inefficiency even in the presence of free entry. We enrich the standard model with heterogeneous firms so that preferences are non-separable in off market time and market consumption and show that this changes the welfare implications of markup heterogeneity. In this context, homogeneity of markups is neither necessary nor sufficient for efficiency. The marginal cost of the marginal firm is weakly inefficiently high when off-market time and market cons
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Olson, Hannah, Madeleine Haas, and Megan L. Kavanaugh. State-Level Contraceptive Use and Preferences: Estimates from the US 2022 Behavioral Risk Factor Surveillance System. Guttmacher Institute, 2024. http://dx.doi.org/10.1363/2024.300488.

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Contraception plays a key role in people’s realization of their sexual and reproductive health and well-being. The factors that shape contraceptive behaviors are complex and dynamic, and there is growing recognition among reproductive health service providers and advocates that contraceptive service delivery must prioritize patients’ values and preferences to help them exercise their reproductive autonomy.1 Similarly, research and public health surveillance systems that measure not only contraceptive use and method selection but also contraceptive preferences are best suited to evaluate servic
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Elacqua, Gregory, and Leonardo Rosa. Teacher transfers and the disruption of Teacher Staffing in the City of Sao Paulo. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004737.

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This paper analyzes preferences for certain school attributes among in-service teachers. We explore a centralized matching process in the city of Sao Paulo that teachers must use when transferring schools. Because teachers have to list and rank their preferences for schools, we can estimate the desirability of school attributes using a rank-ordered logit model. We show that the schools distance from the teachers home, school average test scores, and teacher composition play a central role in teacher preferences. Furthermore, we show that preferences vary according to teacher characteristics, s
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