2020
|
7. | Dirk Burkhardt; Kawa Nazemi; Egils Ginters Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations Proceedings Article In: Egils Ginters; Mario Arturo Ruiz Estrada; Miquel Angel Piera Eroles (Ed.): ICTE in Transportation and Logistics 2019, pp. 319–327, Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure, Cham, 2020, ISBN: 978-3-030-39688-6. @inproceedings{10.1007/978-3-030-39688-6_40,
title = {Process Support and Visual Adaptation to Assist Visual Trend Analytics in Managing Transportation Innovations},
author = {Dirk Burkhardt and Kawa Nazemi and Egils Ginters},
editor = {Egils Ginters and Mario Arturo Ruiz Estrada and Miquel Angel Piera Eroles},
url = {https://rd.springer.com/chapter/10.1007%2F978-3-030-39688-6_40, Springer},
doi = {10.1007/978-3-030-39688-6_40},
isbn = {978-3-030-39688-6},
year = {2020},
date = {2020-01-30},
booktitle = {ICTE in Transportation and Logistics 2019},
pages = {319--327},
publisher = {Springer International Publishing. Lecture Notes in Intelligent Transportation and Infrastructure},
address = {Cham},
abstract = {In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In the domain of mobility and logistics, a variety of new technologies and business ideas are arising. Beside technologies that aim on ecologically and economic transportation, such as electric engines, there are also fundamental different approaches like central packaging stations or deliveries via drones. Yet, there is a growing need for analytical systems that enable identifying new technologies, innovations, business models etc. and give also the opportunity to rate those in perspective of business relevance. Commonly adaptive systems investigate only the users' behavior, while a process-related supports could assist to solve an analytical task more efficient and effective. In this article an approach that enables non-experts to perform visual trend analysis through an advanced process support based on process mining is described. This allow us to calculate a process model based on events, which is the baseline for process support feature calculation. These features and the process model enable to assist non-expert users in complex analytical tasks. |
2014
|
6. | Kawa Nazemi Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Reprint by Eugraphics Association (EG)). @phdthesis{Nazemi2014f,
title = {Adaptive Semantics Visualization},
author = {Kawa Nazemi},
url = {https://diglib.eg.org/handle/10.2312/12076, EG Lib
https://diglib.eg.org/bitstream/handle/10.2312/12076/nazemi.pdf, full text},
doi = {10.2312/12076},
year = {2014},
date = {2014-11-27},
school = {Technische Universität Darmstadt},
abstract = {Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users' behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "visualization cockpit". This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.},
note = {Reprint by Eugraphics Association (EG)},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users' behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "visualization cockpit". This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers. |
5. | Kawa Nazemi Adaptive Semantics Visualization PhD Thesis Technische Universität Darmstadt, 2014, (Department of Computer Science. Supervised by Dieter W. Fellner.). @phdthesis{Nazemi2014g,
title = {Adaptive Semantics Visualization},
author = {Kawa Nazemi},
url = {https://tuprints.ulb.tu-darmstadt.de/id/eprint/4319, TU Darmstadt Prints
https://tuprints.ulb.tu-darmstadt.de/4319/1/Nazemi_Diss.pdf, full text},
year = {2014},
date = {2014-11-23},
address = {Darmstadt, Germany},
school = {Technische Universität Darmstadt},
abstract = {Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users’ behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "‘visualization cockpit"’. This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers.},
note = {Department of Computer Science. Supervised by Dieter W. Fellner.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Human access to the increasing amount of information and data plays an essential role for the professional level and also for everyday life. While information visualization has developed new and remarkable ways for visualizing data and enabling the exploration process, adaptive systems focus on users’ behavior to tailor information for supporting the information acquisition process. Recent research on adaptive visualization shows promising ways of synthesizing these two complementary approaches and make use of the surpluses of both disciplines. The emerged methods and systems aim to increase the performance, acceptance, and user experience of graphical data representations for a broad range of users. Although the evaluation results of the recently proposed systems are promising, some important aspects of information visualization are not considered in the adaptation process. The visual adaptation is commonly limited to change either visual parameters or replace visualizations entirely. Further, no existing approach adapts the visualization based on data and user characteristics. Other limitations of existing approaches include the fact that the visualizations require training by experts in the field.
In this thesis, we introduce a novel model for adaptive visualization. In contrast to existing approaches, we have focused our investigation on the potentials of information visualization for adaptation. Our reference model for visual adaptation not only considers the entire transformation, from data to visual representation, but also enhances it to meet the requirements for visual adaptation. Our model adapts different visual layers that were identified based on various models and studies on human visual perception and information processing. In its adaptation process, our conceptual model considers the impact of both data and user on visualization adaptation. We investigate different approaches and models and their effects on system adaptation to gather implicit information about users and their behavior. These are than transformed and applied to affect the visual representation and model human interaction behavior with visualizations and data to achieve a more appropriate visual adaptation. Our enhanced user model further makes use of the semantic hierarchy to enable a domain-independent adaptation.
To face the problem of a system that requires to be trained by experts, we introduce the canonical user model that models the average usage behavior with the visualization environment. Our approach learns from the behavior of the average user to adapt the different visual layers and transformation steps. This approach is further enhanced with similarity and deviation analysis for individual users to determine similar behavior on an individual level and identify differing behavior from the canonical model. Users with similar behavior get similar visualization and data recommendations, while behavioral anomalies lead to a lower level of adaptation. Our model includes a set of various visual layouts that can be used to compose a multi-visualization interface, a sort of "‘visualization cockpit"’. This model facilitates various visual layouts to provide different perspectives and enhance the ability to solve difficult and exploratory search challenges. Data from different data-sources can be visualized and compared in a visual manner. These different visual perspectives on data can be chosen by users or can be automatically selected by the system.
This thesis further introduces the implementation of our model that includes additional approaches for an efficient adaptation of visualizations as proof of feasibility. We further conduct a comprehensive user study that aims to prove the benefits of our model and underscore limitations for future work. The user study with overall 53 participants focuses with its four conditions on our enhanced reference model to evaluate the adaptation effects of the different visual layers. |
4. | Kawa Nazemi; Dirk Burkhardt; Reimond Retz; Arjan Kuijper; Jörn Kohlhammer Adaptive Visualization of Linked-Data Proceedings Article In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Ryan McMahan; Jason Jerald; Hui Zhang; Steven M Drucker; Chandra Kambhamettu; Maha El Choubassi; Zhigang Deng; Mark Carlson (Ed.): Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., pp. 872–883, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14364-4. @inproceedings{Nazemi2014b,
title = {Adaptive Visualization of Linked-Data},
author = {Kawa Nazemi and Dirk Burkhardt and Reimond Retz and Arjan Kuijper and Jörn Kohlhammer},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},
url = {https://link.springer.com/chapter/10.1007/978-3-319-14364-4_84, Springer link},
doi = {10.1007/978-3-319-14364-4_84},
isbn = {978-3-319-14364-4},
year = {2014},
date = {2014-03-01},
booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},
pages = {872--883},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS 8888},
abstract = {Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Adaptive visualizations reduces the required cognitive effort to comprehend interactive visual pictures and amplify cognition. Although the research on adaptive visualizations grew in the last years, the existing approaches do not consider the transformation pipeline from data to visual representation for a more efficient and effective adaptation. Further todays systems commonly require an initial training by experts from the field and are limited to adaptation based either on user behavior or on data characteristics. A combination of both is not proposed to our knowledge. This paper introduces an enhanced instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on content, visual layout, visual presentation, and visual interface. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonical requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling. |
3. | Kawa Nazemi; Dirk Burkhardt; Wilhelm Retz; Jörn Kohlhammer Adaptive Visualization of Social Media Data for Policy Modeling Proceedings Article In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Ryan McMahan; Jason Jerald; Hui Zhang; Steven M Drucker; Chandra Kambhamettu; Maha El Choubassi; Zhigang Deng; Mark Carlson (Ed.): Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing., pp. 333–344, Springer International Publishing, Cham, 2014, ISBN: 978-3-319-14249-4. @inproceedings{Nazemi2014,
title = {Adaptive Visualization of Social Media Data for Policy Modeling},
author = {Kawa Nazemi and Dirk Burkhardt and Wilhelm Retz and Jörn Kohlhammer},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Ryan McMahan and Jason Jerald and Hui Zhang and Steven M Drucker and Chandra Kambhamettu and Maha El Choubassi and Zhigang Deng and Mark Carlson},
url = {https://link.springer.com/chapter/10.1007/978-3-319-14249-4_32, Springer link},
doi = {10.1007/978-3-319-14249-4_32},
isbn = {978-3-319-14249-4},
year = {2014},
date = {2014-01-01},
booktitle = {Proceeding of the International Symposium on Visual Computing (ISVC 2014). Advances in Visual Computing.},
pages = {333--344},
publisher = {Springer International Publishing},
address = {Cham},
series = {LNCS 8887},
abstract = {The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The visual analysis of social media data emerged a huge number of interactive visual representations that use different characteristics of the data to enable the process of information acquisition. The social data are used in the domain of policy modeling to gather information about citizens' demands, opinions, and requirements and help to decide about political policies. Although existing systems already provide a huge number of visual analysis tools, the search and exploration paradigm is not really clear. Furthermore, the systems commonly do not provide any kind of human centered adaptation for the different stakeholders involved in the policy making process. In this paper, we introduce a novel approach that investigates the exploration and search paradigm from two different perspectives and enables a visual adaptation to support the exploration and analysis process. |
2013
|
2. | Kawa Nazemi; Reimond Retz; Jürgen Bernard; Jörn Kohlhammer; Dieter Fellner Adaptive Semantic Visualization for Bibliographic Entries Proceedings Article In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Baoxin Li; Fatih Porikli; Victor Zordan; James Klosowski; Sabine Coquillart; Xun Luo; Min Chen; David Gotz (Ed.): Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing., pp. 13–24, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, ISBN: 978-3-642-41939-3. @inproceedings{Nazemi2013,
title = {Adaptive Semantic Visualization for Bibliographic Entries},
author = {Kawa Nazemi and Reimond Retz and Jürgen Bernard and Jörn Kohlhammer and Dieter Fellner},
editor = {George Bebis and Richard Boyle and Bahram Parvin and Darko Koracin and Baoxin Li and Fatih Porikli and Victor Zordan and James Klosowski and Sabine Coquillart and Xun Luo and Min Chen and David Gotz},
url = {https://link.springer.com/chapter/10.1007/978-3-642-41939-3_2, Springer link},
doi = {10.1007/978-3-642-41939-3_2},
isbn = {978-3-642-41939-3},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of International Symposium on Visual Computing (ISVC 2013). Advances in Visual Computing.},
pages = {13--24},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
series = {LNCS 8034},
abstract = {Adaptive visualizations aim to reduce the complexity of visual representations and convey information using interactive visualizations. Although the research on adaptive visualizations grew in the last years, the existing approaches do not make use of the variety of adaptable visual variables. Further the existing approaches often premises experts, who has to model the initial visualization design. In addition, current approaches either incorporate user behavior or data types. A combination of both is not proposed to our knowledge. This paper introduces the instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on visual layout and visual presentation in a multiple visualization environment. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonic requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Adaptive visualizations aim to reduce the complexity of visual representations and convey information using interactive visualizations. Although the research on adaptive visualizations grew in the last years, the existing approaches do not make use of the variety of adaptable visual variables. Further the existing approaches often premises experts, who has to model the initial visualization design. In addition, current approaches either incorporate user behavior or data types. A combination of both is not proposed to our knowledge. This paper introduces the instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on visual layout and visual presentation in a multiple visualization environment. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonic requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling. |
2011
|
1. | Matthias Breyer; Kawa Nazemi; Christian Stab; Dirk Burkhardt; Arjan Kuijper A Comprehensive Reference Model for Personalized Recommender Systems Book Chapter In: M. J. Smith; G. Salvendy (Ed.): Human Interface and the Management of Information. Interacting with Information: Symposium on Human Interface 2011, Orlando, FL, USA., pp. 528–537, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, ISBN: 978-3-642-21793-7. @inbook{Breyer2011,
title = {A Comprehensive Reference Model for Personalized Recommender Systems},
author = {Matthias Breyer and Kawa Nazemi and Christian Stab and Dirk Burkhardt and Arjan Kuijper},
editor = {M. J. Smith and G. Salvendy},
url = {https://link.springer.com/chapter/10.1007%2F978-3-642-21793-7_60},
doi = {10.1007/978-3-642-21793-7_60},
isbn = {978-3-642-21793-7},
year = {2011},
date = {2011-01-01},
booktitle = {Human Interface and the Management of Information. Interacting with Information: Symposium on Human Interface 2011, Orlando, FL, USA.},
pages = {528--537},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
abstract = {Existing reference models for recommender systems are on an abstract level of detail or do not point out the processes and transitions of recommendation systems. However, this information is relevant for developers to design or improve recommendation systems. Even so, users need some background information of the calculation process to understand the process and accept or configure these systems proper. In this paper we present a comprehensive reference model for recommender systems which conjuncts the recommendation processes on an adequate level of detail. To achieve this, the processes of content-based and collaboration-based systems are merged and extended by the transitions and phases of hybrid systems. Furthermore, the algorithms which can be applied in the phases of the model are examined to identify the data flow between these phases. With our model those information of the recommendation calculation process can be identified, which encourages the traceability and thus the acceptance of recommendations.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Existing reference models for recommender systems are on an abstract level of detail or do not point out the processes and transitions of recommendation systems. However, this information is relevant for developers to design or improve recommendation systems. Even so, users need some background information of the calculation process to understand the process and accept or configure these systems proper. In this paper we present a comprehensive reference model for recommender systems which conjuncts the recommendation processes on an adequate level of detail. To achieve this, the processes of content-based and collaboration-based systems are merged and extended by the transitions and phases of hybrid systems. Furthermore, the algorithms which can be applied in the phases of the model are examined to identify the data flow between these phases. With our model those information of the recommendation calculation process can be identified, which encourages the traceability and thus the acceptance of recommendations. |