Dynamic radius for context- aware recommender system
In the field of transportation and smart cities, context-aware recommendation is a trending subject that targets users’ satisfaction and comfort. The subject has developed from e-commerce to cover different domains including path planning at urban areas. Researchers were competed in delivering the l...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor's University
2021
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/93999/7/93999_Dynamic%20radius%20for%20context.pdf http://irep.iium.edu.my/93999/ http://jestec.taylors.edu.my |
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| Summary: | In the field of transportation and smart cities, context-aware recommendation is a trending subject that targets users’ satisfaction and comfort. The subject has developed from e-commerce to cover different domains including path planning at urban areas. Researchers were competed in delivering the level of contribution in many areas. Search radius is considered a major pillar at any context-aware recommender framework, however, in many studies the search radius is not having a major contribution in the delivered systems. In this paper, a dynamic search radius algorithm is introduced as part of a context-aware recommender framework. Agglomerations and competition effects are used to enhance search radius results. Deep neural network is a major artificial intelligence method used in this research to tackle cold-start problem and to improve recommendation outcomes. |
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