Brisk Logic was drawn closer by an organization to manufacture a movement proposal motor on AI and ML models for their locale-based travel mobile application. Through this application, clients could take outings to close by places, investigate the network and so on. Presently, they needed to make the application all the more captivating and customized by giving travel proposals to clients.
For this our experts of data scientist have implemented an efficient travel recommendation system where:
● Clients were prescribed spots to visit dependent on their past movement history
● Clients were shown a list of close-by vacation spots when they visit a specific spot.
● Clients were shown recommendations dependent on their preferences and inclinations and furthermore refreshes from different travelers who had comparable interests.
● We utilized Machine learning models to assemble recommendation engines and afterward prepared the calculation to investigate key information focuses.
● The tech stack we implemented in building these Machine Learning models was Python, Tensorflow, Sklearn, iOS CoreML, Elasticsearch.
As a result, we built a hybrid recommendation model that was both content-based and collaborative.