Relevant bibliographies by topics / Users’ preferences

Academic literature on the topic 'Users’ preferences'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Users’ preferences"

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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 divided into short-term and long-term interest models. The short-term interest reflects the user’s current preference, and the long-term interest refers to the user’s interest from all his historical behaviors, representing the user’s consistent and stable preference. Users’ dynamic preference is obtained by integrating short-term interest and long-term interest, which solves the problem that the user’s preference cannot reflect the change in the user’s interest in real-time. We use the public Amazon review dataset to test the model we propose in the paper. Our model achieves the best performance, with a maximum performance improvement of 15.21% compared with the basic model (BPR, NCF) and 2.04% compared with the sequence model (GRU4REC, Caser, etc.), which proves that the user’s dynamic preference model can effectively predict the user’s dynamic preference. Users’ dynamic preferences are helpful in predicting users’ real-time preferences, especially in the field of recommendation.
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Shi, Yancui, Jianhua Cao, Congcong Xiong, and Xiankun Zhang. "A Prediction Method of Mobile User Preference Based on the Influence between Users." International Journal of Digital Multimedia Broadcasting 2018 (July 19, 2018): 1–12. http://dx.doi.org/10.1155/2018/8081409.

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User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.
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Park, Han-Saem, Moon-Hee Park, and Sung-Bae Cho. "Mobile Information Recommendation Using Multi-Criteria Decision Making with Bayesian Network." International Journal of Information Technology & Decision Making 14, no. 02 (March 2015): 317–38. http://dx.doi.org/10.1142/s0219622015500017.

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The advancement of network technology and the popularization of the Internet lead to increased interest in information recommendation. This paper proposes a group recommendation system that takes the preferences of group users in mobile environment and applies the system to recommendation of restaurants. The proposed system recommends the restaurants by considering various preferences of multiple users. To cope with the uncertainty in mobile environment, we exploit Bayesian network, which provides reliable performance and models individual user's preference. Also, Analytical Hierarchy Process of multi-criteria decision-making method is used to estimate the group users' preference from individual users' preferences. Experiments in 10 different situations provide a comparison of the proposed method with random recommendation, simple rule-based recommendation and neural network recommendation, and confirm that the proposed method is useful with the subjective test.
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Nadi, S., and A. H. Houshyaripour. "A NEW MODEL FOR FUZZY PERSONALIZED ROUTE PLANNING USING FUZZY LINGUISTIC PREFERENCE RELATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 417–21. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-417-2017.

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This paper proposes a new model for personalized route planning under uncertain condition. Personalized routing, involves different sources of uncertainty. These uncertainties can be raised from user’s ambiguity about their preferences, imprecise criteria values and modelling process. The proposed model uses Fuzzy Linguistic Preference Relation Analytical Hierarchical Process (FLPRAHP) to analyse user’s preferences under uncertainty. Routing is a multi-criteria task especially in transportation networks, where the users wish to optimize their routes based on different criteria. However, due to the lake of knowledge about the preferences of different users and uncertainties available in the criteria values, we propose a new personalized fuzzy routing method based on the fuzzy ranking using center of gravity. The model employed FLPRAHP method to aggregate uncertain criteria values regarding uncertain user’s preferences while improve consistency with least possible comparisons. An illustrative example presents the effectiveness and capability of the proposed model to calculate best personalize route under fuzziness and uncertainty.
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Zha, Yongfu, Yongjian Zhang, Zhixin Liu, and Yumin Dong. "Self-Attention Based Time-Rating-Aware Context Recommender System." Computational Intelligence and Neuroscience 2022 (September 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/9288902.

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The sequential recommendation can predict the user’s next behavior according to the user’s historical interaction sequence. To better capture users’ preferences, some sequential recommendation models propose time-aware attention networks to capture users’ long-term and short-term intentions. However, although these models have achieved good results, they ignore the influence of users on the rating information of items. We believe that in the sequential recommendation, the user’s displayed feedback (rating) on an item reflects the user’s preference for the item, which directly affects the user’s choice of the next item to a certain extent. In different periods of sequential recommendation, the user’s rating of the item reflects the change in the user’s preference. In this paper, we separately model the time interval of items in the user’s interaction sequence and the ratings of the items in the interaction sequence to obtain temporal context and rating context, respectively. Finally, we exploit the self-attention mechanism to capture the impact of temporal context and rating context on users’ preferences to predict items that users would click next. Experiments on three public benchmark datasets show that our proposed model (SATRAC) outperforms several state-of-the-art methods. The Hit@10 value of the SATRAC model on the three datasets (Movies-1M, Amazon-Movies, Amazon-CDs) increased by 0.73%, 2.73%, and 1.36%, and the NDCG@10 value increased by 5.90%, 3.47%, and 4.59%, respectively.
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Chen, Pengzhan, Jihua Wu, and Ning Li. "A Personalized Navigation Route Recommendation Strategy Based on Differential Perceptron Tracking User’s Driving Preference." Computational Intelligence and Neuroscience 2023 (January 4, 2023): 1–14. http://dx.doi.org/10.1155/2023/8978398.

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With the increasing frequency of autonomous driving, more and more attention is paid to personalized path planning. However, the path selection preferences of users will change with internal or external factors. Therefore, this paper proposes a personalized path recommendation strategy that can track and study user’s path preference. First, we collect the data of the system, establish the relationship with the user preference factor, and get the user’s initial preference weight vector by dichotomizing the K-means algorithm. The system then determines whether user preferences change based on a set threshold, and when the user’s preference changes, the current preference weight vector can be obtained by redefining the preference factor or calling difference perception. Finally, the road network is quantized separately according to the user preference weight vector, and the optimal path is obtained by using Tabu search algorithm. The simulation results of two scenarios show that the proposed strategy can meet the requirements of autopilot even when user preferences change.
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Žnidaršič, Martin, Aljaž Osojnik, Peter Rupnik, and Bernard Ženko. "Improving Effectiveness of a Coaching System through Preference Learning." Technologies 10, no. 1 (January 31, 2022): 24. http://dx.doi.org/10.3390/technologies10010024.

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The paper describes an approach for indirect data-based assessment and use of user preferences in an unobtrusive sensor-based coaching system with the aim of improving coaching effectiveness. The preference assessments are used to adapt the reasoning components of the coaching system in a way to better align with the preferences of its users. User preferences are learned based on data that describe user feedback as reported for different coaching messages that were received by the users. The preferences are not learned directly, but are assessed through a proxy—classifications or probabilities of positive feedback as assigned by a predictive machine learned model of user feedback. The motivation and aim of such an indirect approach is to allow for preference estimation without burdening the users with interactive preference elicitation processes. A brief description of the coaching setting is provided in the paper, before the approach for preference assessment is described and illustrated on a real-world example obtained during the testing of the coaching system with elderly users.
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Thijssen, Kirsten, Marion Vlemminx, Michelle Westerhuis, Jeanne Dieleman, M. Beatrijs Van der Hout-Van der Jagt, and S. Guid Oei. "Uterine Monitoring Techniques from Patients' and Users' Perspectives." American Journal of Perinatology Reports 08, no. 03 (July 2018): e184-e191. http://dx.doi.org/10.1055/s-0038-1669409.

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Objective To evaluate preferences from patients and users on 3 uterine monitoring techniques, during labor. Study Design Women in term labor were simultaneously monitored with the intrauterine pressure catheter, the external tocodynamometer, and the electrohysterograph. Postpartum, these women filled out a questionnaire evaluating their preferences and important aspects. Nurses completed a questionnaire evaluating users' preferences. Results Of all 52 participating women, 80.8% preferred the electrohysterograph, 17.3% the intrauterine pressure catheter and 1.9% the external tocodynamometer. For these women, the electrohysterograph scored best regarding application and presence during labor (p < 0.001). Most important aspects were “least likely to harm” and “least discomfort”. Of 57 nurses, 40.4% preferred the electrohysterograph, 35.1% the external tocodynamometer, and 24.6% had no preference, or replied that their preference is subject to situation and patient. Conclusion Patients prefer the electrohysterograph over the external tocodynamometer and the intrauterine pressure catheter, while healthcare providers report ambiguous results.
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Bok, Kyoungsoo, Jinwoo Song, Jongtae Lim, and Jaesoo Yoo. "Personalized Search Using User Preferences on Social Media." Electronics 11, no. 19 (September 24, 2022): 3049. http://dx.doi.org/10.3390/electronics11193049.

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In contrast to traditional web search, personalized search provides search results that take into account the user’s preferences. However, the existing personalized search methods have limitations in providing appropriate search results for the individual’s preferences, because they do not consider the user’s recent preferences or the preferences of other users. In this paper, we propose a new search method considering the user’s recent preferences and similar users’ preferences on social media analysis. Since the user expresses personal opinions on social media, it is possible to grasp the user preferences when analyzing the records of social media activities. The proposed method collects user social activity records and determines keywords of interest using TF-IDF. Since user preferences change continuously over time, we assign time weights to keywords of interest, giving many high values to state-of-the-art user preferences. We identify users with similar preferences to extend the search results to be provided to users because considering only user preferences in personalized searches can provide narrow search results. The proposed method provides personalized search results considering social characteristics by applying a ranking algorithm that considers similar user preferences as well as user preferences. It is shown through various performance evaluations that the proposed personalized search method outperforms the existing methods.
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Wu, Li, and Ma. "A Comparative Study of Spatial and Temporal Preferences for Waterfronts in Wuhan based on Gender Differences in Check-In Behavior." ISPRS International Journal of Geo-Information 8, no. 9 (September 14, 2019): 413. http://dx.doi.org/10.3390/ijgi8090413.

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The geographical location and check-in frequency of social platform users indicate their personal preferences and intentions for space. On the basis of social media data and gender differences, this study analyzes Weibo users’ preferences and the reasons behind these preferences for the waterfronts of the 21 major lakes within Wuhan’s Third Ring Road, in accordance with users’ check-in behaviors. According to the distribution characteristics of the waterfronts’ points of interest, this study explores the preferences of male and female users for waterfronts and reveals, through the check-in behaviors of Weibo users, the gender differences in the preference and willingness of these users to choose urban waterfronts. Results show that men and women check in significantly more frequently on weekends than on weekdays. Women are more likely than men to check in at waterfronts. Significant differences in time and space exist between male and female users’ preferences for different lakes.
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Dissertations / Theses on the topic "Users’ preferences"

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Shin, Jongu. "Modeling users' powertrain preferences." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62670.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 79).
Our goal is to construct a system that can determine a drivers preferences and goals and perform appropriate actions to aid the driver achieving his goals and improve the quality of his road behavior. Because the recommendation problem could be achieved effectively once we know the driver's intention, in this thesis, we are going to solve the problem to determine the driver's preferences. A supervised learning approach has already been applied to this problem. However, because the approach locally classify a small interval at a time and is memoryless, the supervised learning does not perform well on our goal. Instead, we need to introduce new approach which has following characteristics. First, it should consider the entire stream of measurements. Second, it should be tolerant to the environment. Third, it should be able to distinguish various intentions. In this thesis, two different approaches, Bayesian hypothesis testing and inverse reinforcement learning, will be used to classify and estimate the user's preferences. Bayesian hypothesis testing classifies the driver as one of several driving types. Assuming that the probability distributions of the features (i.e. average, standard deviation) for a short period of measurement are different among the driving types, Bayesian hypothesis testing classifies the driver as one of driving types by maintaining a belief distribution for each driving type and updating it online as more measurements are available. On the other hand, inverse reinforcement learning estimates the users' preferences as a linear combination of driving types. The inverse reinforcement learning approach assumes that the driver maximizes a reward function while driving, and his reward function is a linear combination of raw / expert features. Based on the observed trajectories of representative drivers, apprenticeship learning first calculates the reward function of each driving type with raw features, and these reward functions serve as expert features. After, with observed trajectories of a new driver, the same algorithm calculates the reward function of him, not with raw features, but with expert features, and estimates the preferences of any driver in a space of driving types.
by Jongu Shin.
M.Eng.
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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 usuaris en temes que són compartits en plataformes de microblogging. Hem pres com a cas d'estudi la participació digital de tweeters a la política. Els mètodes proposats a la Part II busquen definir un paper pels usuaris de les Xarxes Socials, ja sigui com a creadors o generadors de contingut i distribuïdors o consumidors de contingut. Hem plantejat un mètode on usuaris amb interessos similars però amb diferent rols són agrupats en una mateixa comunitat, de manera que els nous continguts es propaguen més ràpidament.
El objetivo de esta tesis es desarrollar nuevos y diversos métodos para modelar las preferencias de los usuarios en las Redes Sociales Online. Los métodos propuestos tienen como finalidad ser aplicados en áreas de investigación como la Personalización o Recomendación de ítems y la Detección de Grupos de Usuarios con gustos similares. Dichos métodos pueden ser agrupados en dos tipos: i) métodos basados en técnicas de análisis de texto (Parte I, Capítulos del 3 al 5) y ii) métodos basados en teoría de grafos (Parte II, Capítulos 6 y 7). Con los métodos planteados en la Parte I es posible determinar el nivel de interés de los usuarios en temas que son compartidos en plataformas de microblogging. Hemos tomado como caso de estudio la participación digital de tweeters en la política. Los métodos propuestos en la Parte II buscan definir un rol para los usuarios en Redes Sociales, ya sea como creadores o generadores de contenido y distribuidores o consumidores de contenido. Hemos planteado un método donde usuarios con intereses similares pero con distinto rol, son agrupados en una misma comunidad de forma que nuevo contenido se propague más rápidamente.
The objective of this thesis is to develop new and diverse methods to model the preferences of the users in the Online Social Networks. The proposed methods are intended to be applied in areas of research such as Personalization or Recommendation of items and the detection of groups of users with similar tastes. These methods can be grouped into two types: i) methods based on text analysis techniques (Chapters 3 to 5) and ii) methods based on graph theory (Chapters 6 and 7). With the methods proposed in i) it is possible to determine the level of interest of users on topics that are shared on microblogging platforms. We have taken as a case study the digital participation of tweeters in politics. The methods proposed in ii) seek to define a role for users in social networks, whether as creators or content generators and distributors or content consumers. We have proposed a method where users with similar interests but with different roles, are grouped in the same community so that new content spreads more quickly.
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MacBean, Anna Ruth. "Apparent Preferences of Beach Users at Virginia Beach Resort Zone." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/19299.

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After compiling an appropriate list of beach criteria from established award programs and experts, the research landscape architect observed the Virginia Beach Resort Zone for areas of intense beach user activity.  The resulting analysis of these "hot-spots" indicated that urban resort beach users visiting the recreational beach during high-use times tended to gravitate toward locations on the resort beach which were close to three needs:  public parking, public restrooms, and inexpensive refreshments.  This pattern shows the apparent preferences of many beach users for certain amenities.
Master of Landscape Architecture
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Mignot, Helen R. "Users and accounting information preferences of government department financial reports." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1996. https://ro.ecu.edu.au/theses/936.

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The introduction of an accounting standard requiring government departments to replace fund-type, cash-based accounting statements with business-type, accrual based accounting statements has led to criticism that business-type, general purpose financial statements do not take account of the information requirements of major users. Such criticism echoes a long standing debate in which the users of public sector financial statements and their informational requirements are analysed in competing models. One view suggests that there are many users with homogeneous informational needs, who can be classified into a few broad groups. The other view maintains that there are few users who have differential informational requirements. This research adds to the few empirical studies on the usefulness of public sector accounting statement information. The purpose of this research is to test the hypothesis that users perceive that there is no difference in the usefulness of fund-type, cash-based; business-type, accrual-based accounting statements, and both cash and accrual combined accounting statements. Responses from legislators, citizen/ interest group members, and preparers to a questionnaire provides the data for statistical analysis. Test results suggest that there is only moderate support for the hypothesis that heterogenous users have different information needs. Strong support is found for the hypothesis that combined sets of statements as opposed to cash, or accrual are more useful. This conclusion holds for both the importance and useability dimensions of the construct perceived usefulness.
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Bhoompally, Rohit. "Analysis of business ranking for a connected group of Yelp users by aggregating preference pairs." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439308101.

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Seymour, Zakiya Ayo-Zahra. "Understanding what sanitation users value - examining preferences and behaviors for sanitation systems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52168.

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Over the last two decades, sanitation policy and development has undergone a paradigm shift away from heavily-subsidized, supply-driven approaches towards behavioral-based demand-driven approaches. These current approaches to increase sanitation demand are multi-faceted, requiring multiple stakeholders with varying degrees of interest, knowledge, and capacity. Although efforts exist to increase sanitation access by incorporating engineering design principles with implementation planning approaches, these groups generally work independently without strong connections, thus reducing the potential of their impact. As a result, the design of appropriate sanitation technology is disengaged from the implementation of acceptable technology into communities, disconnecting user preference integration from sanitation technology design and resulting in fewer sanitation technologies being adopted and used. To address these challenges in developing successful interventions, this research examined how user preferences for specific attributes of appropriate sanitation technologies and their respective implementation arrangements influence their adoption and usage. Data for the study included interviews of 1002 sanitation users living in a peri-urban area of South Africa; the surveyed respondents were asked about their existing sanitation technology, their preferences for various sanitation technology design attributes, as well as their perspectives on current and preferred sanitation implementation arrangements. The data revealed that user acceptability of appropriate sanitation technology is influenced by the adoption classification of the users. Through the identification of motives and barriers to sanitation usage that were statistically significant, it exhibited the need to differentiate users who share private sanitation from those use communal sanitation facilities. Results also indicated that user acceptability of appropriate sanitation systems is dependent on the technical design attributes of sanitation. The development of utility functions detailed the significance of seven technical design attributes and determined their respective priorities. An agent-based simulation examined how user preferences for sanitation technology design and implementation influence its adoption and usage. Findings suggest that user acceptability of sanitation technology is dependent on both the technology design and the implementation arrangement being preferred.
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Pukawan, Kriangsak. "The Attitudes and Preferences of Internet Users in Thailand Toward Online Privacy Rights." NSUWorks, 2006. http://nsuworks.nova.edu/gscis_etd/781.

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This study strives to solicit and assimilate the attitudes and preferences of online users in Thailand concerning their privacy rights. It is designed to resolve this noticeable void in Thai information technology policy. Three sub problems are separately investigated by use of a questionnaire used to discern Thai uneasiness about (1) the clandestine commercial collection and dissemination of personal online data profiles, (2) a wide variety of online criminal activities that should merit government regulation and intervention, and (3) Internet users' trust in their government to curtail online criminal activities. Scholarly research in Thailand has not previously probed or examined these issues. Statistical methods employed in this study involve the use of "sign test" procedures, since no restrictive speculations are assumed about the population distribution. A questionnaire is employed to elicit replies from a database consisting of knowledgeable Thai Internet users. The resultant outcomes are based on a statistical, nationwide sampling plan that draws its responses from computer literate students at four major regionally diverse public universities. The results reveal that discernable and deep-rooted attitudes of Internet users in Thailand toward online privacy rights exist. Further, the results indicate that online patrons have significant preferences about perceived invasions of their privacy on the Internet. The analyses of these discernible traits provide a groundbreaking profile about Internet users in Thailand that merits further investigations in the future. The findings and implications of this study should provide the cornerstone for legislative bodies, telecommunication regulators, policing authorities, and a wide variety of cyberspace consumers to adhere to these concerns, and to enhance the orderly expansion of advanced Internet technologies in Thailand. Ultimately, the resolution of these matters should contribute to the development of a useful IT infrastructure that will provide fairness to both sides of the Internet equation, benefiting both Internet users and online commercial vendors justly.
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Kolivodiakos, Paraskevas. "Evaluating End Users’ Online Privacy Preferences and Identifying PET Design Requirements: A Literature Review." Thesis, Luleå tekniska universitet, Datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-67720.

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In this research end user privacy preferences regarding online resources web and mobile applications and websites are investigated and design requirements needed for the development of a privacy focused, privacy enhancing technology tool are identified, as derived from the literature, the crowd source based solution is the most appealing solution so it is fully analyzed according to our research main focus.
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Schaap, Robbert-Jan [Verfasser], and Florian [Akademischer Betreuer] Diekert. "The Dynamic Preferences and Incentives of Natural Resource Users / Robbert-Jan Schaap ; Betreuer: Florian Diekert." Heidelberg : Universitätsbibliothek Heidelberg, 2021. http://d-nb.info/1234460602/34.

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Wang, Feng. "Inferring users' multi-attribute preferences from the reviews for augmenting recommender systems in e-commerce." HKBU Institutional Repository, 2016. http://repository.hkbu.edu.hk/etd_oa/336.

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By now, people are accustomed to getting some personalized recommendations when they are finding movies to watch, music to listen, and so on. All of these recommendations come from recommender systems, and can aid the process of the decision making to avoid the problem of "information overload". Over the years, there has been much work done both in industry and academia on developing new approaches for recommender systems. However, there are still some hurdles in adapting recommender systems to a broader range of real-life applications. In the e-commerce environment especially with the so called high-risk products (also called high-cost or high-involvement products, such as digital cameras, computers, and cars), because a user does not buy the high-risk product very often, it is normal that s/he is not able to rate many products. For the same reason, the current buyer is often a new user because s/he would not afford to buy the same kind of high-risk product before. The traditional recommender techniques (such as user-based collaborative filtering and content-based methods) can thus not be effectively applicable in this environment, because they largely assume that the users have prior experiences with products. Thus, the "data sparsity" and "new users" are two typical challenging issues that the classical recommender systems cannot well address in high-risk product domains. In some recommender systems, a new user will be asked to indicate his/her preferences on some aspects in order to address the so called cold-start problem via collecting some preferences. Such collected preferences are usually not complete due to unfamilaring with the product domain, which are called partial preferences.;In this thesis, we propose to leverage some auxiliary data of online reviewers' opinions, so as to enrich the partial preferences. With the objective of developing more effective recommender systems for high-risk products in e-commerce, in our work, we have exerted to derive reviewers' preferences from the textual reviews they posted. Then, these recovered preferences are leveraged to estimate and supplement a new buyer's preference with which the product recommendation is produced. Firstly, we propose a novel clustering method based on Latent Class Regression model (LCRM), which is able to consider both the overall ratings and feature-level opinion values (as extracted from textual reviews) to infer individual reviewers' weight feature preferences, that represent the weights the user places on different product features. Secondly, we propose a method to estimate reviewers' value preferences (i.e., the user's preferences on the product's attribute values) by matching their review opinions to the corresponding attributes' static specifications. Thirdly, we investigate how to combine weight preferences and value preferences to model user preferences based on Multi-Attribute Utility Theory (MAUT) with the purpose of providing higher quality product recommendations. Particularly, it was shown from our experimental studies that the incorporation of review information can significantly enhance the recommendation accuracy, relative to those without considering reviews. As the practical implication, our proposed solutions can be usefully plugged into an online system to be adopted in real-ecommerce sites.
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Books on the topic "Users’ preferences"

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E, Watson Alan, and Intermountain Research Station (Ogden, Utah), eds. Visitor characteristics and preferences for three national forest wildernesses in the south. Ogden, Utah: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1992.

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Bowker, James M. Mountain biking at Tsali: An assessment of users, preferences, conflicts, and management alternatives. Asheville, NC: USDA Forest Service, Southern Research Station, 2002.

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N, Cole David. Wilderness visitors, experiences, and management preferences: How they vary with use level and length of stay. Fort Collins, Colo: United States Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2008.

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Najjar, Yaser M. Recreational preferences among State Park users in New England: A case study of the Massachusetts State Park system. Keene. N.H: Keene State College, 1992.

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Mortensen, Dennis R. Yahoo! Web Analytics. New York: John Wiley & Sons, Ltd., 2009.

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Yahoo! Web analytics: Tracking, reporting, and analyzing for data-driven insights. Indianapolis, Ind: Wiley Technology Pub., 2009.

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Working with preferences: Less is more. Heidelberg: Springer, 2011.

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Kamwana, Laston L. M. Results of a tree seed user preference survey. [Zomba, Malawi]: Forestry Research Institute of Malawi, 1997.

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Bussey, Shelagh Christine. Public uses, preferences and perceptions of urban woodlands in Redditch. Birmingham: University of Central England in Birmingham, 1996.

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Sänn, Alexander. The Preference-Driven Lead User Method for New Product Development. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-17263-3.

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Book chapters on the topic "Users’ preferences"

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Mahrt, Merja. "Values and Genre Preferences." In Values of German Media Users, 107–16. Wiesbaden: VS Verlag für Sozialwissenschaften, 2010. http://dx.doi.org/10.1007/978-3-531-92256-0_6.

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Mahrt, Merja. "Channel Loyalty and Genre Preferences." In Values of German Media Users, 117–26. Wiesbaden: VS Verlag für Sozialwissenschaften, 2010. http://dx.doi.org/10.1007/978-3-531-92256-0_7.

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Knijnenburg, Bart P., Reza Ghaiumy Anaraky, Daricia Wilkinson, Moses Namara, Yangyang He, David Cherry, and Erin Ash. "User-Tailored Privacy." In Modern Socio-Technical Perspectives on Privacy, 367–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82786-1_16.

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AbstractModern information systems require their users to make a myriad of privacy decisions, but users are often neither motivated nor capable of managing this deluge of decisions. This chapter covers the concept of tailoring the privacy of an information system to each individual user. It discusses practical problems that may arise when collecting data to determine a user’s privacy preferences, techniques to model these preferences, and a number of adaptation strategies that can be used to tailor the system’s privacy practices, settings, or interfaces to the user’s modeled preferences. Throughout the chapter, we provide recommendations on how to develop user-tailored privacy solutions, depending on the requirements and characteristics of the system and its users.
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Oku, Kenta, Ta Son Tung, and Fumio Hattori. "Collaborative Filtering for Predicting Users’ Potential Preferences." In Knowledge-Based and Intelligent Information and Engineering Systems, 44–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23866-6_5.

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Gupta, Saurabh, and Sutanu Chakraborti. "UtilSim: Iteratively Helping Users Discover Their Preferences." In Lecture Notes in Business Information Processing, 113–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39878-0_11.

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Kumamoto, Tadahiko, Tomoya Suzuki, and Hitomi Wada. "Visualizing Impression-Based Preferences of Twitter Users." In Social Computing and Social Media, 209–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07632-4_20.

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Dietz, Linus W., Sameera Thimbiri Palage, and Wolfgang Wörndl. "Navigation by Revealing Trade-offs for Content-Based Recommendations." In Information and Communication Technologies in Tourism 2022, 149–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94751-4_14.

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AbstractConversational recommender systems have been introduced to provide users the opportunity to give feedback on items in a turn-based dialog until a final recommendation is accepted. Tourism is a complex domain for recommender systems because of high cost of recommending a wrong item and often relatively few ratings to learn user preferences. In a scenario such as recommending a city to visit, conversational content-based recommendation may be advantageous, since users often struggle to specify their preferences without concrete examples. However, critiquing item features comes with challenges. Users might request item characteristics during recommendation that do not exist in reality, for example demanding very high item quality for a very low price. To tackle this problem, we present a novel conversational user interface which focuses on revealing the trade-offs of choosing one item over another. The recommendations are driven by a utility function that assesses the user’s preference toward item features while learning the importance of the features to the user. This enables the system to guide the recommendation through the search space faster and accurately over prolonged interaction. We evaluated the system in an online study with 600 participants and find that our proposed paradigm leads to improved perceived accuracy and fewer conversational cycles compared to unit critiquing.
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Venkatanathan, Jayant, Denzil Ferreira, Michael Benisch, Jialiu Lin, Evangelos Karapanos, Vassilis Kostakos, Norman Sadeh, and Eran Toch. "Improving Users’ Consistency When Recalling Location Sharing Preferences." In Human-Computer Interaction – INTERACT 2011, 380–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23774-4_31.

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Rentto, Katja, Ilkka Korhonen, Antti Väätänen, Lasse Pekkarinen, Timo Tuomisto, Luc Cluitmans, and Raimo Lappalainen. "Users’ Preferences for Ubiquitous Computing Applications at Home." In Lecture Notes in Computer Science, 384–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39863-9_29.

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Ahmed, Sultan. "Collaborative Filtering with Users’ Qualitative and Conditional Preferences." In Advances in Artificial Intelligence, 403–6. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57351-9_45.

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Conference papers on the topic "Users’ preferences"

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Zhang, Lu, Zhu Sun, Ziqing Wu, Jie Zhang, Yew Soon Ong, and Xinghua Qu. "Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/521.

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Existing studies on next point-of-interest (POI) recommendation mainly attempt to learn user preference from the past and current sequential behaviors. They, however, completely ignore the impact of future behaviors on the decision-making, thus hindering the quality of user preference learning. Intuitively, users' next POI visits may also be affected by their multi-step future behaviors, as users may often have activity planning in mind. To fill this gap, we propose a novel Context-aware Future Preference inference Recommender (CFPRec) to help infer user future preference in a self-ensembling manner. In particular, it delicately derives multi-step future preferences from the learned past preference thanks to the periodic property of users' daily check-ins, so as to implicitly mimic user’s activity planning before her next visit. The inferred future preferences are then seamlessly integrated with the current preference for more expressive user preference learning. Extensive experiments on three datasets demonstrate the superiority of CFPRec against state-of-the-arts.
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Jameson, Anthony, Silvia Gabrielli, and Antti Oulasvirta. "Users' preferences regarding intelligent user interfaces." In Proceedingsc of the 13th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1502650.1502734.

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Du, Pengyu, Kin Wai Michael Siu, and Yi-Teng Shih. "Product Style Preferences: An Image-based User Study Software Concept." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001715.

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In the market, once producers of a particular product category become mature in their production technology, their products will have few functional differences. Thus, the greatest challenge for designers today lies in developing an appropriate design language that fits the tastes of target users. Designers use many user-study methods (interviews, questionnaires, focus groups) to understand their target users’ tastes. However, these methods mainly rely on language as the core medium of interaction. Because language can be subjective and one-sided, it is difficult to describe abstract concepts such as style preferences. In addition, in such design research, language-based information is transferred from target users to design researchers to designers over several rounds, and the objectivity and accuracy of such information can decrease substantially because of differences in people’s interpretations. This paper reviews product styling-related user study methods and technologies and proposes an image-based user study software concept that minimizes the above problems. This proposed software uses images as its main medium of interaction between target users and designers. It applies artificial intelligence technology to analyze target users’ common style preference patterns based on their image choices and sorting results. The software’s output is each target user’s persona in the form of a perceptual map and mood board. These personas provide objective product style preferences directly from the target users. This software can thus provide designers with intuitive and accurate references and inspire them to design products that meet users’ tastes and improve user experience.
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MacDonald, Erin, Richard Gonzalez, and Panos Papalambros. "Preference Inconsistency in Multidisciplinary Design Decision Making." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35580.

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Research from behavioral psychology and experimental economics asserts that individuals construct preferences on a case-by-case basis when called to make a decision. A common, implicit assumption in engineering design is that user preferences exist a priori. Thus, preference elicitation methods used in design decision making can lead to preference inconsistencies across elicitation scenarios. This paper offers a framework for understanding preference inconsistencies, within and across individual users. We give examples of three components of this new framework: comparative, internal, and external inconsistencies across users. The examples demonstrate the impact of inconsistent preference construction on common engineering and marketing design methods, including discrete choice analysis, modeling stated vs. revealed preferences, and the Kano method and thus QFD. Exploring and explaining preference inconsistencies produces new understandings of the relationship between user and product.
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Seimetz, Valentin, Rebecca Eifler, and Jörg Hoffmann. "Learning Temporal Plan Preferences from Examples: An Empirical Study." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/572.

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Temporal plan preferences are natural and important in a variety of applications. Yet users often find it difficult to formalize their preferences. Here we explore the possibility to learn preferences from example plans. Focusing on one preference at a time, the user is asked to annotate examples as good/bad. We leverage prior work on LTL formula learning to extract a preference from these examples. We conduct an empirical study of this approach in an oversubscription planning context, using hidden target formulas to emulate the user preferences. We explore four different methods for generating example plans, and evaluate performance as a function of domain and formula size. Overall, we find that reasonable-size target formulas can often be learned effectively.
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Reinecke, Katharina, and Abraham Bernstein. "Predicting user interface preferences of culturally ambiguous users." In Proceeding of the twenty-sixth annual CHI conference extended abstracts. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1358628.1358841.

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Neidhardt, Julia, Rainer Schuster, Leonhard Seyfang, and Hannes Werthner. "Eliciting the users' unknown preferences." In the 8th ACM Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2645710.2645767.

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Yu, Zeping, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, and Xing Xie. "Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/585.

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User modeling is an essential task for online recommender systems. In the past few decades, collaborative filtering (CF) techniques have been well studied to model users' long term preferences. Recently, recurrent neural networks (RNN) have shown a great advantage in modeling users' short term preference. A natural way to improve the recommender is to combine both long-term and short-term modeling. Previous approaches neglect the importance of dynamically integrating these two user modeling paradigms. Moreover, users' behaviors are much more complex than sentences in language modeling or images in visual computing, thus the classical structures of RNN such as Long Short-Term Memory (LSTM) need to be upgraded for better user modeling. In this paper, we improve the traditional RNN structure by proposing a time-aware controller and a content-aware controller, so that contextual information can be well considered to control the state transition. We further propose an attention-based framework to combine users' long-term and short-term preferences, thus users' representation can be generated adaptively according to the specific context. We conduct extensive experiments on both public and industrial datasets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.
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Ujházi, Tamás. "Modelling Users’ Preferences Towards Autonomous Vehicles." In Challenges in Economics and Business in the Post-COVID Times. University of Maribor Press, 2022. http://dx.doi.org/10.18690/um.epf.5.2022.27.

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The demand for autonomous vehicles (AVs) will most likely grow in the upcoming years, thus revolutionising transport. As highlighted by KPMG and many academics, one of the key factors of the mass implementation of AVs is their public acceptance. As a result, there is a growing body of literature investigating consumer acceptance of AVs. The authors of these studies mostly use various versions of the available technology acceptance models. However, there is a limitation to using these models to investigate the consumer acceptance of AVs, since they assume that the respondents have real-life experience of using the given technology, which is not the case with AVs. In this pilot research, the author uses conjoint analysis, which is widely applied to better understand users’ preferences during product development and research. The product attributes are formed according to the independent variables of the model of the Unified Theory of Acceptance and Use of Technology. Eighteen cards were created with a combination of different attribute levels. These cards were evaluated by 202 respondents in two steps. It was found that this research model is capable of measuring users’ preferences towards AVs, and safety is the most important factor in the decision-making process.
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Izmir Tunahan, G., H. Altamirano, and J. Unwin Teji. "THE ROLE OF DAYLIGHT IN LIBRARY USERS’ SEAT PREFERENCES." In CIE 2021 Conference. International Commission on Illumination, CIE, 2021. http://dx.doi.org/10.25039/x48.2021.op24.

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Seating that meets the needs and preferences of students can promote a longer stay in libraries and keep students motivated, which in turn influences their emotions and learning abilities. However, existing knowledge on the interaction between daylighting and seating preferences is limited. This study aims to understand what type of spaces are in more demand and the relationship between seat occupancy and daylight availability. Occupancy data of the UCL Bartlett library acquired from motion sensors located underneath each desk was used to assess occupancy, which was then compared to characteristics of space, including daylight availability. The study revealed that although daylight has a considerable impact on students’ seat selection, the seating preference of the students cannot be explained by daylight alone. The seats with a good combination of daylight, outdoor view and privacy are in more demand compared to seats that provide only a high level of daylight. Future research should involve individual perception in addition to occupancy monitoring data, considering daylight conditions together with other components such as privacy, outdoor views, and quietness.
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Reports on the topic "Users’ preferences"

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Bansal, Prateek, Akanksha Sinha, Rubal Dua, and Ricardo Daziano. Eliciting Preferences of Ride-Hailing Users and Drivers. King Abdullah Petroleum Studies and Research Center, February 2020. http://dx.doi.org/10.30573/ks--2020-dp03.

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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, September 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 ride-hailing. The paper builds on results from the LCA to reflect on conditions of advantage and disadvantage that may make ride-hailing attractive and beneficial for particular social groups. The paper identifies four unique clusters: Carless middle-income ride-hailing users, Disadvantaged non-users, Young middle-class non-users, and Advantaged ride-hailing users. The research uses data on such perceptions to draw insights that may inform commercial and policy decisions. Findings suggest that issues such as the perception of legality in ride-hailing and aversion to crime play a significant role in the choice of such a mode in the context of Bogotá, particularly among socially and transport advantaged users.
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Bowker, J. Michael, and Donald B. K. English. Mountain Biking at Tsali: An Assessment of Users, Preferences, Conflicts, and Management Alternatives. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2002. http://dx.doi.org/10.2737/srs-gtr-59.

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Bowker, J. Michael, and Donald B. K. English. Mountain Biking at Tsali: An Assessment of Users, Preferences, Conflicts, and Management Alternatives. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 2002. http://dx.doi.org/10.2737/srs-gtr-59.

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Morrison, Laura, Anushah Hossain, Myles Elledge, Brian Stoner, and Jeffrey Piascik. User-Centered Guidance for Engineering and Design of Decentralized Sanitation Technologies. RTI Press, June 2018. http://dx.doi.org/10.3768/rtipress.2018.rb.0017.1806.

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Technological innovations in sanitation are poised to address the great need for sanitation improvements in low-income countries. Worldwide, more than 2.4 billion people lack access to improved sanitation facilities. Innovative waste treatment and sanitation technologies aim to incorporate user-centered findings into technology engineering and design. Without a focus on users, even the most innovative technology solutions can encounter significant barriers to adoption. Drawing on a household survey conducted in urban slum communities of Ahmedabad, India, this research brief identifies toilet and sanitation preferences, amenities, and attributes that might promote adoption of improved sanitation technologies among potential user populations. This work uses supplemental insights gained from focus groups and findings from the literature. Based on our research, we offer specific guidance for engineering and design of sanitation products and technologies.
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Sabogal-Cardona, Orlando, Lynn Scholl, Daniel Oviedo, Amado Crotte, and Felipe Bedoya. Not My Usual Trip: Ride-hailing Characterization in Mexico City. Inter-American Development Bank, August 2021. http://dx.doi.org/10.18235/0003516.

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With a few exceptions, research on ride-hailing has focused on North American cities. Previous studies have identified the characteristics and preferences of ride-hailing adopters in a handful of cities. However, given their marked geographical focus, the relevance and applicability of such work to the practice of transport planning and regulation in cities in the Global South is minimal. In developing cities, the entrance of new transport services follows very different trajectories to those in North America and Europe, facing additional social, economic, and cultural challenges, and involving different strategies. Moreover, the determinants of mode choice might be mediated by social issues such as the perception of crime and the risk of sexual harassment in public transportation, which is often experienced by women in large cities such as Mexico. This paper examines ride-hailing in the Metropolitan Area of Mexico City, unpacking the characteristics of its users, the ways they differ from users of other transport modes, and the implications for urban mobility. Building on the household travel survey from 2017, our analytical approach is based on a set of categorical models. Findings suggest that gender, age, education, and being more mobile are determinants of ride-hailing adoption. The analysis shows that ride-hailing is used for occasional trips, and it is usually done for leisure and health trips as well as for night trips. The study also reflects on ride-hailings implications for the way women access the city.
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Hofstetter, Patrick, Barbara C. Lippiatt, Jane C. Bare, and Amy S. Rushing. User preferences for life-cycle decision support tools:. Gaithersburg, MD: National Institute of Standards and Technology, 2002. http://dx.doi.org/10.6028/nist.ir.6874.

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Tracy, Jenny, Arne Jacobson, and Evan Mills. Quality and Performance of LED Flashlights in Kenya: Common End User Preferences and Complaints. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/985242.

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Athey, Susan, Dean Karlan, Emil Palikot, and Yuan Yuan. Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces. Cambridge, MA: National Bureau of Economic Research, November 2022. http://dx.doi.org/10.3386/w30633.

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Gnutzmann-Mkrtchyan, Arevik, and Jules Hugot. Gravity-Based Tools for Assessing the Impact of Tariff Changes. Asian Development Bank, February 2022. http://dx.doi.org/10.22617/wps220053-2.

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The first tool estimates the impact on bilateral trade for 5,020 products in a partial equilibrium framework. The second quantifies the general equilibrium impact on bilateral aggregate trade, allowing estimates of trade reallocation and welfare changes. The paper uses these to estimate the impact for Armenia of tariff changes including (i) alignment with the external tariff of the Eurasian Economic Union, (ii) free trade agreements between the Eurasian Economic Union and other economies—Iran and the People’s Republic of China, and (iii) loss of beneficiary status for the European Union’s Generalised Scheme of Preferences.
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