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Incorporating user design preferences into multi-objective roof truss optimizationBailey, Breanna Michelle Weir 17 September 2007 (has links)
Automated systems for large-span roof truss optimization provide engineers with
the flexibility to consider multiple alternatives during conceptual design. This
investigation extends previous work on multi-objective roof truss optimization to include
the design preferences of a human user. The incorporation of user preferences into the
optimization process required creation of a mechanism to identify and model preferences
as well as discovery of an appropriate location within the algorithm for preference
application.
The first stage of this investigation developed a characteristic feature vector to
describe the physical appearance of an individual truss. The feature vector translates
visual elements of a truss into quantifiable properties transparent to the computer
algorithm. The nine elements in the feature vector were selected from an assortment of
geometrical and behavioral factors and describe truss simplicity, general shape, and
chord shape.
Using individual feature vectors, a truss population may be divided into groups
of similar design. Partitioning the population simplifies the feedback process by allowing users to identify groups that best suit their design preferences. Several
unsupervised clustering mechanisms were evaluated for their ability to generate truss
classifications that matched human judgment and minimized intra-group deviation. A
one-dimensional Kohonen self-organizing map was selected.
The characteristic feature vectors of truss designs within user-selected groups
provided a basis for determining whether or not a user would like a new design. After
analyzing user inputs, prediction algorithm trials sought to reproduce these inputs and
apply them to the prediction of acceptable designs. This investigation developed a
hybrid method combining rough set reduct techniques and a back-propagation neural
network.
This hybrid prediction mechanism was embedded into the operations of an
Implicit Redundant Representation Genetic Algorithm. Locations within the ranking
and selection processes of this algorithm formed the basis of a study to investigate the
effect of user preference on truss optimization.
Final results for this investigation prove that incorporating a user's aesthetic
design preferences into the optimization project generates more design alternatives for
the user to examine; that these alternatives are more in line with a user's conceptual
perception of the project; and that these alternatives remain structurally optimal.
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Location Aware Multi-criteria Recommender System for Intelligent Data MiningValencia Rodríguez, Salvador 18 October 2012 (has links)
One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the most suitable items for a specific user, based on the user’s preferences and constraints. The latter are the focus of this thesis. While Recommender Systems have been successful in many domains, a number of challenges remain. For example, most implementations consider only single criteria ratings, and consequently are unable to identify why a user prefers an item over others. Many systems classify the user into one single group or cluster which is an unrealistic approach, since in real world users share commonalities in different degrees with diverse types of users. Others require a large amount of previously gathered data about users’ interactions and preferences, in order to be successfully applied. In this study, we introduce a methodology for the creation of Personalized Multi Criteria Context Aware Recommender Systems that aims to overcome these shortcomings. Our methodology incorporates the user’s current context information, and techniques from the Multiple Criteria Decision Analysis (MCDA) field of study to analyze and model the user preferences. To this end, we create a multi criteria user preference model to assess the utility of each item for a specific user, to then recommend the items with the highest utility. The criteria considered when creating the user preference model are the user’s location, mobility level and user profile. The latter is obtained by considering the user specific needs, and generalizing the user data from a large scale demographic database. We present a case study where we applied our methodology into PeRS, a personal Recommender System to recommend events that will take place within the Ottawa/Gatineau Region. Furthermore, we conduct an offline experiment performed to evaluate our methodology, as implemented in our case study. From the experimental results we conclude that our RS is capable to accurately narrow down, and identify, the groups from a demographic database where a user may belong, and subsequently generate highly accurate recommendation lists of items that match with his/her preferences. This means that the system has the ability to understand and typify the user. Moreover, the results show that the obtained system accuracy doesn’t depend on the user profile. Therefore, the system is potentially capable to produce equally accurate recommendations for a wide range of the population.
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Location Aware Multi-criteria Recommender System for Intelligent Data MiningValencia Rodríguez, Salvador 18 October 2012 (has links)
One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the most suitable items for a specific user, based on the user’s preferences and constraints. The latter are the focus of this thesis. While Recommender Systems have been successful in many domains, a number of challenges remain. For example, most implementations consider only single criteria ratings, and consequently are unable to identify why a user prefers an item over others. Many systems classify the user into one single group or cluster which is an unrealistic approach, since in real world users share commonalities in different degrees with diverse types of users. Others require a large amount of previously gathered data about users’ interactions and preferences, in order to be successfully applied. In this study, we introduce a methodology for the creation of Personalized Multi Criteria Context Aware Recommender Systems that aims to overcome these shortcomings. Our methodology incorporates the user’s current context information, and techniques from the Multiple Criteria Decision Analysis (MCDA) field of study to analyze and model the user preferences. To this end, we create a multi criteria user preference model to assess the utility of each item for a specific user, to then recommend the items with the highest utility. The criteria considered when creating the user preference model are the user’s location, mobility level and user profile. The latter is obtained by considering the user specific needs, and generalizing the user data from a large scale demographic database. We present a case study where we applied our methodology into PeRS, a personal Recommender System to recommend events that will take place within the Ottawa/Gatineau Region. Furthermore, we conduct an offline experiment performed to evaluate our methodology, as implemented in our case study. From the experimental results we conclude that our RS is capable to accurately narrow down, and identify, the groups from a demographic database where a user may belong, and subsequently generate highly accurate recommendation lists of items that match with his/her preferences. This means that the system has the ability to understand and typify the user. Moreover, the results show that the obtained system accuracy doesn’t depend on the user profile. Therefore, the system is potentially capable to produce equally accurate recommendations for a wide range of the population.
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Location Aware Multi-criteria Recommender System for Intelligent Data MiningValencia Rodríguez, Salvador January 2012 (has links)
One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the most suitable items for a specific user, based on the user’s preferences and constraints. The latter are the focus of this thesis. While Recommender Systems have been successful in many domains, a number of challenges remain. For example, most implementations consider only single criteria ratings, and consequently are unable to identify why a user prefers an item over others. Many systems classify the user into one single group or cluster which is an unrealistic approach, since in real world users share commonalities in different degrees with diverse types of users. Others require a large amount of previously gathered data about users’ interactions and preferences, in order to be successfully applied. In this study, we introduce a methodology for the creation of Personalized Multi Criteria Context Aware Recommender Systems that aims to overcome these shortcomings. Our methodology incorporates the user’s current context information, and techniques from the Multiple Criteria Decision Analysis (MCDA) field of study to analyze and model the user preferences. To this end, we create a multi criteria user preference model to assess the utility of each item for a specific user, to then recommend the items with the highest utility. The criteria considered when creating the user preference model are the user’s location, mobility level and user profile. The latter is obtained by considering the user specific needs, and generalizing the user data from a large scale demographic database. We present a case study where we applied our methodology into PeRS, a personal Recommender System to recommend events that will take place within the Ottawa/Gatineau Region. Furthermore, we conduct an offline experiment performed to evaluate our methodology, as implemented in our case study. From the experimental results we conclude that our RS is capable to accurately narrow down, and identify, the groups from a demographic database where a user may belong, and subsequently generate highly accurate recommendation lists of items that match with his/her preferences. This means that the system has the ability to understand and typify the user. Moreover, the results show that the obtained system accuracy doesn’t depend on the user profile. Therefore, the system is potentially capable to produce equally accurate recommendations for a wide range of the population.
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Uživatelské preference v prostředí prodejních webů / User preferences in the domain of web shopsPeška, Ladislav January 2011 (has links)
The goal of the thesis is first to find available information about user preferences, user feedback and their acquisition, processing, storing etc. The collected information is then used for making suggestions / advices for the creating an recommender system for the web shops (with special emphasis on implicit feedback). The following chapters introduces UPComp - our solution of the recommender system for the web shops. The UPComp is written in the programming language PHP and uses MySQL database. The thesis also includes testing of the UPComp on real-user web shop sites slantour.cz and antikvariat-ichtys.cz.
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Doporučování se zaměřením na kulturní portály / Recommender systems for culture eventsVytisková, Zuzana January 2017 (has links)
The diploma thesis deals with the topic of recommendation in culture. In the theoretical part, it compares the recommendation of digitally available works with event recommendations, which serves as the basis for describing recommendations on the cultural portal. Further, the thesis examines the domain model as several different interconnected types of objects. Using these relations to enrich data sets allows overcoming the low data density and improving the recommendations. The paper examines two common situations of practical recommendation, general user recommendation with minimal profile and recommendation to registered users with known history. As a part of the solution, hybrid algorithms have been implemented based on the introducing content information into existing collaborative filtering methods. The results are verified in offline tests on data sets consisting of both research and real-world data. The subjective quality of the resulting recommendations was examined through a user study.
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Evaluating the Effectiveness and Efficiency of Real Time Data Visualization : An Action Research StudyMogili, Anusha, Pallapu, Manoj Kumar January 2020 (has links)
Background. In today’s competitive world, dealing with real-time streaming data is a difficult task to be achieved by many organizations. The importance of real time streaming data is rapidly increasing in all software industries by passing time. For quick growth of the companies, the data should be analysed immediately as data will be changing in fraction of second. The huge data will be generated every day and it will lead to problems such as overload of resources, Performance delays etc.., Which in turn will impact behaviour of the system. Finding the problem area in real time is difficult task to achieve as the data changes every second. Dealing with detection of bottlenecks and making decisions to handle the problem area, based on the real time data has been slow over the past years. It is also complicated due to time and effort required for storing and analysing. Organizations are not intended to wait for decision making information up to weeks or months. Organizations need to make an timely-accurate decisions by detecting problem area, in real time to improve their business support systems behaviour and performance. One of the better solutions is through data visualization as an approach. The visualizations are developed and evaluated by using task based approach. The data is collected using interviews and paper survey, to obtain the effective and efficient visualization in detecting bottlenecks. Objectives. The main objective is to find the most effective and efficient data visualization technique for real time streaming data to detect potential bottlenecks. Methods. In this research study, an action research is opted to answer the objectives. We have used interviews and paper survey to collect data in the terms of performance time, accuracy rate and user preference. Data analysis is performed using the Statistical tests and Narrative analysis method. Results. The final results obtained are the effective and efficient visualization techniques based on less performance time, higher accuracy rate and better user preference. Conclusions. An effective and efficient visualization technique for detection of bottlenecks is obtained for real time streaming data. Different categories of tasks has been used to obtain accurate results.
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Aesthetics in User Interface Design: : The Influence on Users' Preference, Decoding and LearningLund, Linda January 2015 (has links)
The question of the relationship between, and the importance of usability and aesthetics, in the field of user interface design, has been debated back and forth. It has also been looked at from different perspectives since Raskin (1994) wrote his article on intuitive design. Several experiments have also been conducted over the last twenty years to find out exactly how much each factor matter, what the ultimate user preference is, and if it can be stereotyped. The more complex part of the discussion, however, seems to be the definitions: exactly what is aesthetics, what is usability and how do they affect each other? To find out, I explored the context of these factors from multiple perspectives, to draw the larger conclusions about what affects what. How accurate is the concept of halo when it comes to interface design; can a less aesthetic interface discourage users from exploring its content? Moreover, can a highly usable interface convince its users that the web page is also aesthetically pleasing? In this paper I will explain the ideas of aesthetic and intuitive design based on two fields of study; human computer interaction design and interaction design. That is in the pursuance of understanding user preference and the design decisions behind one of the most popular interfaces on the internet today.
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Enhancing usability of e-commerce platforms by utilizing the usability factors : An investigation into user preferences.Bahareh, Beyk January 2015 (has links)
Availability of internet to a wide audiences has revolutionized how business is performed. Businesses now use e-commerce to trade products and services. The growth of e-commerce has been dramatically rapid among developed countries. Therefore, the adoption of e-commerce platforms is studied in these countries. Meanwhile less attentions has been given to developing countries. Developing countries can take advantage of lessons learned in the developed countries. One of the major success factors of e-commerce platforms in developed countries is improving the usability of e-commerce platform by considering user’s preferences. User preferences can be defined as feelings and attitudes of users for the interface and functional design of the ecommerce platform. This influences user’s decision and behavior. Considering the users preferences within an e-commerce platform enhances user’s satisfaction and increases user’s loyalty to the platform. To evaluate the user preferences a selection of usability factors are usually studied. Avicenna Research Institute (ARI) is currently considering the development of an e-commerce platform. For this ARI is investigating ways to consider users preferences in the design. This study aims to satisfy this goal. In this study previous research are analyzed, user’s preferences are identified, common used e-commerce platforms are investigated, and ARI’s perspective is included in the analysis. Using these information a set of recommendations on how to improve the usability of an e-commerce platform is given. For this task based approaches are used in form of participatory heuristic evaluation and observations. Interview is used to obtain ARI’s perspective on usability. The study has identified ten usability factors affecting the usability of the e-commerce platform based on user’s preferences. These include consistency, learnability, navigability, simplicity, readability, content relevance, supportability, interactivity, credibility, and telepresence. In addition a set of eighty evaluation criteria are presented to evaluate these factors. Using a qualitative approach, the study has analyzed all these factors in a multiple-case study. The analysis includes input from the users in form of observations, comments and questionnaire. This is combined with input from ARI in form of interviews. Finally the study concludes by providing a path for future research.
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User Privacy Perception and Concerns Regarding the Use of Cloud-Based AssistantsAwojobi, Abiodun 12 1900 (has links)
Cloud-based assistants like the Google Home and the Amazon Alexa have become ubiquitous in our homes. Users can simply communicate with the devices using a smartphone application. There are privacy concerns associated with cloud-based assistants. For example, users do not know what type of information is being sent to the device manufacturer, if the device is stealthily listening to conversations, data retention, or who else has access to the data. Privacy is about perception. The goal of this study is to determine user privacy concerns regarding cloud-based assistants by adopting a quantitative research method. The study used a privacy decision framework that lists three core components, which are technology controls, understanding user privacy preference, and government regulations. The research used Dervin's sensemaking model to describe users' privacy perception using the privacy decision framework and improved on a privacy perception survey instrument from previous dissertations. An understanding of user privacy concerns with cloud-based assistants is required to provide a comprehensive privacy guidance to stakeholders. The significance of this study is in the identification of the privacy perception of users of cloud-based assistants and the extent to which the components of the theoretical framework can impact user privacy perception. The results of this study serve as a guide for device manufacturers and other stakeholders in prioritizing privacy design decisions.
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