Unsupervised Methods for a Personalised Route Recommendation System
| Autor: | Jiri Fajtl, Lorenzo Vitali, Dorothy Monekosso, Paolo Remagnino, Vasileios Argyriou |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Minor (linear algebra) 02 engineering and technology 010501 environmental sciences Recommender system Machine learning computer.software_genre 01 natural sciences Statistical classification Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Artificial intelligence business Guidance system computer 0105 earth and related environmental sciences |
| Zdroj: | CSNDSP |
| Popis: | There are many vehicle navigation systems in existence. They provide directions following routes based on speed, traffic, major or minor roads and places of interest. Although guidance systems are getting increasingly more sophisticated, to our knowledge none of them can provide a recommendation based on subjective preferences. This contribution studies classification algorithms that can provide this very feature. Given all possible paths between geographical locations, the proposed method classifies the available paths, making possible the choice of a trajectory with subjective characteristics. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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