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EXPERIMENTAL ANALYSIS OF DESIGN CHOICES IN MULTIATTRIBUTE UTILITY COLLABORATIVE FILTERING.

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Title: EXPERIMENTAL ANALYSIS OF DESIGN CHOICES IN MULTIATTRIBUTE UTILITY COLLABORATIVE FILTERING.
Authors: MANOUSELIS, NIKOS1 nikosm@ieee.org, COSTOPOULOU, CONSTANTINA1 tina@aua.gr
Source: International Journal of Pattern Recognition & Artificial Intelligence. Mar2007, Vol. 21 Issue 2, p311-331. 21p. 4 Charts, 4 Graphs.
Subjects: Decision making, Technical specifications, Industrial design, Customization, Algorithms
Abstract: Recommender systems have already been engaging multiple criteria for the production of recommendations. Such systems, referred to as multicriteria recommenders, demonstrated early the potential of applying Multi-Criteria Decision Making (MCDM) methods to facilitate recommendation in numerous application domains. On the other hand, systematic implementation and testing of multicriteria recommender systems in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined the importance of carrying out careful testing and parameterization of a recommender system, before it is actually deployed in a real setting. In this paper, the experimental analysis of several design options for three proposed multiattribute utility collaborative filtering algorithms is presented for a particular application context (recommendation of e-markets to online customers), under conditions similar to the ones expected during actual operation. The results of this study indicate that the performance of recommendation algorithms depends on the characteristics of the application context, as these are reflected on the properties of evaluations' data set. Therefore, it is judged important to experimentally analyze various design choices for multicriteria recommender systems, before their actual deployment. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: EXPERIMENTAL ANALYSIS OF DESIGN CHOICES IN MULTIATTRIBUTE UTILITY COLLABORATIVE FILTERING.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. Mar2007, Vol. 21 Issue 2, p311-331. 21p. 4 Charts, 4 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Technical+specifications%22">Technical specifications</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+design%22">Industrial design</searchLink><br /><searchLink fieldCode="DE" term="%22Customization%22">Customization</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
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  Label: Abstract
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  Data: Recommender systems have already been engaging multiple criteria for the production of recommendations. Such systems, referred to as multicriteria recommenders, demonstrated early the potential of applying Multi-Criteria Decision Making (MCDM) methods to facilitate recommendation in numerous application domains. On the other hand, systematic implementation and testing of multicriteria recommender systems in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined the importance of carrying out careful testing and parameterization of a recommender system, before it is actually deployed in a real setting. In this paper, the experimental analysis of several design options for three proposed multiattribute utility collaborative filtering algorithms is presented for a particular application context (recommendation of e-markets to online customers), under conditions similar to the ones expected during actual operation. The results of this study indicate that the performance of recommendation algorithms depends on the characteristics of the application context, as these are reflected on the properties of evaluations' data set. Therefore, it is judged important to experimentally analyze various design choices for multicriteria recommender systems, before their actual deployment. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1142/S021800140700548X
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      – Code: eng
        Text: English
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        PageCount: 21
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      – SubjectFull: Decision making
        Type: general
      – SubjectFull: Technical specifications
        Type: general
      – SubjectFull: Industrial design
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      – SubjectFull: Customization
        Type: general
      – SubjectFull: Algorithms
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      – TitleFull: EXPERIMENTAL ANALYSIS OF DESIGN CHOICES IN MULTIATTRIBUTE UTILITY COLLABORATIVE FILTERING.
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              Text: Mar2007
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              Y: 2007
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