Relevant bibliographies by topics / Predicting user preference

Contents

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

Academic literature on the topic 'Predicting user preference'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Predicting user preference"

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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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Wang, Jenq-Haur, Yen-Tsang Wu, and Long Wang. "Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media." Applied Sciences 11, no. 3 (2021): 1064. http://dx.doi.org/10.3390/app11031064.

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In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion ap
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Yizhi Ren, Lanping Yu, Wei Yang, and Bin Feng. "PPS: A Scheme of Predicting User Preference based on Multivariate." Journal of Next Generation Information Technology 2, no. 3 (2011): 112–21. http://dx.doi.org/10.4156/jnit.vol2.issue3.10.

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Yoon, Yiyeon, Juneseok Hong, and Wooju Kim. "Item recommendation by predicting bipartite network embedding of user preference." Expert Systems with Applications 151 (August 2020): 113339. http://dx.doi.org/10.1016/j.eswa.2020.113339.

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Ambika, Mani, and K. Latha. "Intelligence Based User Profile Generation." Applied Mechanics and Materials 573 (June 2014): 618–23. http://dx.doi.org/10.4028/www.scientific.net/amm.573.618.

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Web intelligence provides a platform that empowers internet users to determine the most appropriate and best information for their interests. It provides the ability to sense and adapt to the needs and preference of the user. The recent advancements have made it conceivable to capture the users experience and interactions with web. Consequently predicting users behaviors will expedite and enhance browsing experience. This paper proposes an intelligent approach for making the web more powerful by predicting the conduct of individual users. The main goal is to implicitly construct user profiles
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Summers, Van, Ken W. Grant, Brian E. Walden, Mary T. Cord, Rauna K. Surr, and Mounya Elhilali. "Evaluation of A “Direct-Comparison” Approach to Automatic Switching In Omnidirectional/Directional Hearing Aids." Journal of the American Academy of Audiology 19, no. 09 (2008): 708–20. http://dx.doi.org/10.3766/jaaa.19.9.6.

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Background: Hearing aids today often provide both directional (DIR) and omnidirectional (OMNI) processing options with the currently active mode selected automatically by the device. The most common approach to automatic switching involves “acoustic scene analysis” where estimates of various acoustic properties of the listening environment (e.g., signal-to-noise ratio [SNR], overall sound level) are used as a basis for switching decisions. Purpose: The current study was carried out to evaluate an alternative, “direct-comparison” approach to automatic switching that does not involve assumptions
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Shi, Yixuan. "Research on Twitter User Tag Preference Prediction Based on Thompson Sampling Algorithm." ITM Web of Conferences 73 (2025): 01014. https://doi.org/10.1051/itmconf/20257301014.

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Twitter's user behaviour data is crucial for studying user patterns and content recommendation. To achieve this goal, the paper first preprocesses a Twitter user dataset obtained from Kaggle. The dataset includes over 40,000 objects in JSON format, focusing on users who tweeted on trending topics and had at least 100 followers and were following at least 100 other accounts. This filtering helps to exclude spam and empty accounts. The study constructs a user-hashtag matrix and applies label encoding technology to convert it into a numerical matrix. The Thompson Sampling algorithm is then applie
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Mukta, Md Saddam Hossain, Euna Mehnaz Khan, Mohammed Eunus Ali, and Jalal Mahmud. "Predicting Movie Genre Preferences from Personality and Values of Social Media Users." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (2017): 624–27. http://dx.doi.org/10.1609/icwsm.v11i1.14910.

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We propose a novel technique to predict a user’s movie genre preference from her psycholinguistic attributes obtained from user social media interactions. In particular, we build machine learning based classification models that take user tweets as input to derive her psychological attributes: personality and value scores, and gives her movie genre preference as output. We train these models using user tweets in Twitter, and her reviews and ratings of movies of different genres in Internet movie database (IMDb). We exploit a key concept of psychology, i.e., an individual’s personality and value
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Ma, Jun, Yuqi Gong, and Wenxia Xu. "Predicting User Preference for Innovative Features in Intelligent Connected Vehicles from a Cultural Perspective." World Electric Vehicle Journal 15, no. 4 (2024): 130. http://dx.doi.org/10.3390/wevj15040130.

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The increasing level of intelligence in automobiles is driving a shift in the human–machine relationship. Users are paying more attention to the intelligent cabin and showing a tendency toward customization. As culture is considered to be an important factor in guiding user behavior and preference, this study innovatively incorporates cultural and human factors into the model to understand how individual cultural orientation influences user preference for innovative human-machine interaction (HMI) features. Firstly, this study considered five Hofstede cultural dimensions as potential impact fa
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Khabiri, Elham, Chiao-Fang Hsu, and James Caverlee. "Analyzing and Predicting Community Preference of Socially Generated Metadata: A Case Study on Comments in the Digg Community." Proceedings of the International AAAI Conference on Web and Social Media 3, no. 1 (2009): 238–41. http://dx.doi.org/10.1609/icwsm.v3i1.13973.

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Large-scale socially-generated metadata is one of the key features driving the growth and success of the emerging Social Web. Recently there have been many research efforts to study the quality of this metadata that relies on quality assessments made by human experts external to a Social Web community. We are interested in studying how an online community itself perceives the relative quality of its own user-contributed content, which has important implications for the successful self-regulation and growth of the Social Web. To this end, we study the community preference for user-contributed c
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Dissertations / Theses on the topic "Predicting user preference"

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Lybecker, Erik. "User preference prediction between ads-supported and subscribed users." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240589.

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The goal of this master’s thesis was to create a model that predicts preference towards a specific exclusive feature in a subscribed service. It investigated unsupervised and semi-supervised learning to identify customer segments that prefer an specific exclusive feature. These customers segments were then used as targets for supervised learning algorithms to predict which segment a user on the ads-supported version would belong to. Two experiments was preformed, one to investigate and identify customer segments with the help of a survey and secondly, the preference prediction. It was found th
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Stensö, Isak, and Andreas Rosenback. "A Random Indexing Approach to User Preference Prediction." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186276.

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Predicting user preferences is a common problem for many companies and services. With the growth of Internet services it becomes both more important and more lucrative being able to predict what products a user would like and then recommend these to them. There are many ways of attempting this, but this study attempts to use random indexing to solve the same problem. Random indexing is a method that has been used successfully when studying the similarity between words, and allows entities to be represented as vectors with relatively small dimensionality. This would allow for fast and memory-ef
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Chan, Matthew D. "Fish ecomorphology predicting habitat preferences of stream fishes from their body shape /." Diss., Connect to this title online, 2001. http://scholar.lib.vt.edu/theses/available/etd-05242001-183326.

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Cha, Seunghyun. "Stochastic space-use prediction in light of spatial choice behaviour : modelling space preference of work-related activities." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709375.

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Lonjarret, Corentin. "Sequential recommendation and explanations." Thesis, Lyon, 2021. http://theses.insa-lyon.fr/publication/2021LYSEI003/these.pdf.

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Ces dernière années, les systèmes de recommandation ont reçu beaucoup d'attention avec l'élaboration de nombreuses propositions qui tirent parti des nouvelles avancées dans les domaines du Machine Learning et du Deep Learning. Grâce à l'automatisation de la collecte des données des actions des utilisateurs tels que l'achat d'un objet, le visionnage d'un film ou le clic sur un article de presse, les systèmes de recommandation ont accès à de plus en plus d'information. Ces données sont des retours implicites des utilisateurs (appelé «~implicit feedback~» en anglais) et permettent de conserver l'
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Tseng, Guo-Chiuan, and 曾國銓. "Applying Deep Learning In User Preference for Rating Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/f6hm8a.

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碩士<br>中原大學<br>資訊管理研究所<br>107<br>The amount of information on the Internet is huge, so that people have difficulty to deal with. Users also need to spend a lot of time to find the needed information. Information overload problem becomes a significant issue for users and online businesses. To resolve this problem, recommender systems are proposed in many researches or applications. The widely used method in recommender systems is the user-based collaborative filtering. It only concerns users’ rating history to analyze users’ preferences. However, users’ text data may contain in users’ preference
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Brandão, André Xavier Ribeiro de Almeida. "Prediction of Privacy Preferences with User Profiles: A Federated Learning Approach." Master's thesis, 2021. https://hdl.handle.net/10216/139326.

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Hsu, Chia-Yu, and 許家瑜. "Rating prediction based on combination of opinion analysis and user preference from textual reviews." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/z6ne2n.

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碩士<br>中原大學<br>資訊管理研究所<br>106<br>The user review websites allows users to share their reviews of products or businesses, give ratings to products or businesses, and interact with other users. Because the rapid growth of online review data, users face the information overload problem. To resolve such problem, many researches proposed various recommendation methods based on the analysis of users’ ratings. Besides users’ ratings, the reviews of products or businesses is unstructured textual data and contain the information of different aspects. These aspects have different impact and importance to
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Su, Ying-Ju, and 蘇盈如. "A Comparison of Rating Prediction Methods based on Review Mining and User Preference Factor Analysis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fd3m57.

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碩士<br>國立交通大學<br>資訊管理研究所<br>106<br>Online review websites not only allow users to share business information and consumer experience, but also the ability to rate, review and influence one another. They help users decide whether to buy products or visit business stores indirectly. However, users are difficult to filter out useful information efficiently due to the overload from a large amount of review information. Accordingly, in order to make accurate predictions for personalizing recommendation systems, it is an important issue to analyze user preferences and predict user ratings by analyzin
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Book chapters on the topic "Predicting user preference"

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Nguyen, Tien T., and John Riedl. "Predicting Users’ Preference from Tag Relevance." In User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38844-6_23.

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Angelastro, S., B. Nadja De Carolis, and S. Ferilli. "Predicting User Preference in Pairwise Comparisons Based on Emotions and Gaze." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22999-3_23.

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Kumar, Deepak, Gopalji Varshney, and Manoj Thakur. "Data Prediction Based on User Preference." In Smart Innovation, Systems and Technologies. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07353-8_66.

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Liu, Mengyang, Cheng Luo, Yiqun Liu, Min Zhang, and Shaoping Ma. "User Preference Prediction in Mobile Search." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68699-8_7.

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Qin, Mian, Scott Buffett, and Michael W. Fleming. "Predicting User Preferences Via Similarity-Based Clustering." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68825-9_22.

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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. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23866-6_5.

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Budel, Gaby, Lennart Hoogenboom, Wouter Kastrop, Nino Reniers, and Flavius Frasincar. "Predicting User Flight Preferences in an Airline E-Shop." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91662-0_19.

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Encheva, Sylvia. "Prediction of New User Preferences with Filtering Techniques." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55038-6_27.

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Mukta, Md Saddam Hossain, Akib Zaman, Md Adnanul Islam, and Bayzid Ashik Hossain. "Predicting Users’ Eat-Out Preference from Big5 Personality Traits." In Third Congress on Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9379-4_37.

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Li, Xiaotong, Yan Tang, Yuan Yuan, and Yingpei Chen. "Predicting User Preferences via Heterogeneous Information Network and Metric Learning." In Knowledge Science, Engineering and Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82136-4_53.

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Conference papers on the topic "Predicting user preference"

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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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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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Shih, Kowei, Zhenghao Deng, Xiang Chen, Yuanzhe Zhang, and Li Zhang. "DST-GFN: A Dual-Stage Transformer Network with Gated Fusion for Pairwise User Preference Prediction in Dialogue Systems." In 2025 8th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2025. https://doi.org/10.1109/aemcse65292.2025.11042684.

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Nabizadeh, Amir Hossein, Alípio Mário Jorge, Suhua Tang, and Yi Yu. "Predicting User Preference Based on Matrix Factorization by Exploiting Music Attributes." In the Ninth International C* Conference. ACM Press, 2016. http://dx.doi.org/10.1145/2948992.2949010.

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Bandara, Syafril, Takeshi Yashiro, M. Fahim Ferdous Khan, Noboru Koshizuka, and Ken Sakamura. "Predicting collective user preference for optimal comfort level in smart buildings." In 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE). IEEE, 2015. http://dx.doi.org/10.1109/gcce.2015.7398686.

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Boon, Yong Yang, Siew Mooi Lim, Annebel Yun Ying Choong, and Hui Yun Chia. "A Song Classifier for Predicting User Preference Based on Spotify Song Attributes." In 2023 13th International Conference on Information Technology in Asia (CITA). IEEE, 2023. http://dx.doi.org/10.1109/cita58204.2023.10262684.

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Ghosh, Dipanjan D., Andrew Olewnik, and Kemper E. Lewis. "An Integrated Framework for Predicting Consumer Choice Through Modeling of Preference and Product Use Data." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68010.

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A critical task in product design is mapping information from consumer space to design space. Currently, this process is largely dependent on the designer to identify and map how psychological and consumer level factors relate to engineered product attributes. In this way, current methodologies lack provision to test a designer’s cognitive reasoning, which could introduce bias while mapping from consumer to design space. Cyber-Empathic Design is a novel framework where user-product interaction data is acquired using embedded sensors. To understand consumer perceptions about a particular produc
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Cheng, Weiyu, Yanyan Shen, Yanmin Zhu, and Linpeng Huang. "DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/462.

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Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this paper proposes a dual-embedding based deep latent factor model named DELF for recommendation with implicit feedback. In addition to
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Yin Dik, Nga, Wai Kei Tsang, Ah Pun Chan, Kwan Yu Lo, and Wai Ching Chu. "Predicting Virtual Garment Fitting Size with Psychographic Characteristics and 3D Body Measurements Using Artificial Neural Network and Visualizing Fitted Bodies Using Generative Adversarial Network." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003635.

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3D virtual garment simulation technology is widely used in apparel industry nowadays with computer-aided manufacturing systems for the earlier stages of apparel design and product development process. The technological advances have brought convenience in garment product fitting procedures with virtual fitting environment, and eventually enhance the supply chain in the aspects of social, economic, and environmental aspects. Many studies have addressed the matters related to non-standardized selection on garment sizing, ease allowance for different selected groups, and use of 3D avatars for vir
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Wu, Bo, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, and Tao Mei. "Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/427.

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Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal
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Reports on the topic "Predicting user preference"

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Moras, Bruno Cesar Krause, Xiaowei Chen, Kenny Chandra Wijaya, Satish Ukkusuri, Samuel Labi, and Konstantina Gkritza. Electric Vehicles: Public Perceptions, Expectations, and Willingness-to-Pay. Purdue University, 2025. https://doi.org/10.5703/1288284317766.

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The primary objective of this project was to understand Indiana resident’s perspectives on electric vehicles (EVs), including adoption incentives and barriers, awareness of adoption incentives, charging preferences, and general travel patterns. A secondary objective was to establish a framework for identifying EV users, detailing their trips, and generating predictions for EV adoption and usage. To achieve these objectives, a stated preference survey was conducted with 1,217 Indiana residents. Two datasets containing travel behavior data were incorporated to generate synthetic data. The survey
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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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Maslo, Brooke, Morgan Mark, Kathleen Kerwin, et al. Habitat use and foraging ecology of bats in Morristown National Historical Park: Effects of invasive vegetation. National Park Service, 2024. http://dx.doi.org/10.36967/2303689.

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Temperate insectivorous bats value high prey abundance and appropriate vegetative structure when selecting foraging habitats. Forests, particularly in the eastern United States, provide prime foraging habitats for bats but can be heavily impacted by non-native plants, which may alter arthropod diversity and abundance, as well as vegetative structure. To investigate associations between non-native plants and insect abundance, vegetative structure, and consequently bat activity, we performed vegetation surveys, insect trapping, and acoustic monitoring at 23 forested plots in northern New Jersey,
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Landau, Sergei Yan, John W. Walker, Avi Perevolotsky, Eugene D. Ungar, Butch Taylor, and Daniel Waldron. Goats for maximal efficacy of brush control. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7587731.bard.

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Background. Brush encroachment constitutes a serious problem in both Texas and Israel. We addressed the issue of efficacy of livestock herbivory - in the form of goat browsing - to change the ecological balance to the detriment of the shrub vegetation. Shrub consumption by goats is kept low by plant chemical defenses such as tannins and terpenes. Scientists at TAES and ARO have developed an innovative, cost-effective methodology using fecal Near Infrared Spectrometry to elucidate the dietary percentage of targeted, browse species (terpene-richredberry and blueberry juniper in the US, and tanni
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Dy, Sydney M., Julie M. Waldfogel, Danetta H. Sloan, et al. Integrating Palliative Care in Ambulatory Care of Noncancer Serious Chronic Illness: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), 2020. http://dx.doi.org/10.23970/ahrqepccer237.

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Objectives. To evaluate availability, effectiveness, and implementation of interventions for integrating palliative care into ambulatory care for U.S.-based adults with serious life-threatening chronic illness or conditions other than cancer and their caregivers We evaluated interventions addressing identification of patients, patient and caregiver education, shared decision-making tools, clinician education, and models of care. Data sources. We searched key U.S. national websites (March 2020) and PubMed®, CINAHL®, and the Cochrane Central Register of Controlled Trials (through May 2020). We a
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L51580 Safety Factors in the Assessment of Realistic Defects in Pipeline Welds. Pipeline Research Council International, Inc. (PRCI), 1998. http://dx.doi.org/10.55274/r0010330.

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The British Standards Institute document PD6493:1980 uses the crack tip opening displacement (CTOD) design curve as a method for determining defect acceptance levels in welded joints. The document is under revision and current proposals for the fracture section of the revised document employ three levels of assessment procedures, arranged as a progression from simple stress treatments producing conservative results, to critical analyses which incorporate more rigorous stress analyses and contain no inherent factors of safety. The proposed Level 1 assessment procedure is generally consistent wi
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