There were two kinds of context that were considered: keywords and photos. One of the key problems for now is the long waiting times. This last approach worked decently: it can recommend 73% of the expected routes. This had some problems with unwanted biases towards cities, so the second attempt switched it around by first clustering on general area to make sure recommendations are dissimilar, and then using clustering and finding big clusters to see which routes are popular. The first attempt to find these routes was using a heatmap to see which routes were popular, and then clustering routes to find dissimilar routes to recommend. The specific task from Relive was to use the Relive database to find popular, dissimilar routes to recommend within an area, and then find context for those recommended routes. They wanted a route recommendation engine for their users, in particular users that are unfamiliar with the area. The client for this project was Relive, a company that has a tracker for outdoor activities with a focus on the experience of the activity. This course has a group of 4 students complete a project for a real-world company (the client). This report describes the Bachelor End Project, a compulsory course for the Bachelor Computer Science at Delft University of Technology. Wilms, Ivo (TU Delft Electrical Engineering, Mathematics and Computer Science) Korpel, Dennis (TU Delft Electrical Engineering, Mathematics and Computer Science) Route Recommendation Engine: Relive your outdoor adventuresĪriës, Alessandro (TU Delft Electrical Engineering, Mathematics and Computer Science)Įsseveld, Jeroen (TU Delft Electrical Engineering, Mathematics and Computer Science)
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