Developing Industrial Case-Based Reasoning Applications

Bergmann, Ralph; Althoff, Klaus-Dieter; Breen, Sibilah; Göker, Mehmet H.; Manago, Michel; Traphöner, Ralph; Wess, Stefan

doi:10.1007/b94998

Cited by 76 publications

(65 citation statements)

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“…The objective of CBR is to solve new problems using solutions that were used to address similar problems [30,31]. The structural CBR approach is one type of case representation that represents cases according to a common structured vocabulary using predefined attributes and values [52]. CBR generally involves four steps [53]: (1) retrieving similar previously experienced cases, (2) reusing cases by copying or integrating the emerging solutions from the retrieved cases, (3) revising or adapting the retrieved solutions to solve a new problem, and (4) retaining a new confirmed or validated solution ( Figure 1).…”

Section: Case Based Reasoningmentioning

confidence: 99%

A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

Bahramian

Abbaspour

Claramunt

2017

Mobile Information Systems

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Nowadays, large amounts of tourism information and services are available over the Web. This makes it difficult for the user to search for some specific information such as selecting a tour in a given city as an ordered set of points of interest. Moreover, the user rarely knows all his needs upfront and his preferences may change during a recommendation process. The user may also have a limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problem. The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user's feedbacks and additional contextual information. It offers personalized tours to the user based on his preferences thanks to the combination of a case based reasoning framework and an artificial neural network. The proposed method has been tried in the city of Tehran in Iran. The results show that the proposed method outperforms current artificial neural network methods and combinations of case based reasoning with -nearest neighbor methods in terms of user effort, accuracy, and user satisfaction.

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Section: Case Based Reasoningmentioning

confidence: 99%

A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

Bahramian

Abbaspour

Claramunt

2017

Mobile Information Systems

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“…For example, the goal of the IN-RECA [8] methodology is to support the development of (industrial) case-based reasoning (CBR) applications. Software process models from existing CBR applications are stored in an experience base that is structured at three levels.…”

Section: Related Workmentioning

confidence: 99%

Modelling with Problem Frames: Explanations and Context in Ambient Intelligent Systems

Kofod-Petersen

Cassens

2011

Lecture Notes in Computer Science

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Abstract. When designing and implementing real world ambient intelligent systems we are in need of applicable information systems engineering methods. The tools we find in the intelligent systems area focus on the knowledge engineering parts, whereas traditional software engineering techniques are usually not designed with the peculiarities of intelligent systems design in mind. This holds in particular for explanation-aware intelligent systems. This work looks at problem frames for explanations and investigates how problem frames can be used to elicit, analyse, and specify these specific requirements. The point of departure is an existing ambient intelligent information system for the hospital ward domain. The work presented here analyses how such a system can be redesigned with a focus on explanation-awareness.

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“…More recently, in the course of the INRECA-II [7] However, most of the applications reported are complete case-based systems that were developed for research purposes or designed by any vendor for covering the needs of a private company with the help of a CBR tool [3] [20]. In most cases, these systems are very adjusted to work well in a specific domain and to change this domain will involve modifying the entire system.…”

Section: Introductionmentioning

confidence: 99%

Multi-domain case-based module for customer support

Heras¹,

García-Pardo²,

Ramos-Garijo³

et al. 2009

Expert Systems with Applications

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Technology Management Centres provide technological and customer support services for private or public organisations. Commonly, these centres offer support using a helpdesk software that facilitates the work of their operators. In this paper, a CBR module that acts as a solution recommender for customer support environments is presented. The CBR module is flexible and multi-domain, in order to be easily integrable with any existing helpdesk software in the company.

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Developing Industrial Case-Based Reasoning Applications

Cited by 76 publications

References 0 publications

A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

Modelling with Problem Frames: Explanations and Context in Ambient Intelligent Systems

Multi-domain case-based module for customer support

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