Recommending accommodations using machine learning provider in a cloud

Recommender systems are present almost everywhere on the web and can be the key to potentially improved business results. In this thesis we develop a production-ready recommender system for a website that offers eco-sustainable accommodations, that meet certain requirements (e.g. usage of solar energy, water filtering and reuse, waste recycling etc.). First we examine crucial big data technologies and some of the cloud-based machine learning platforms. We proceed to choose the best platform and use it to collect data and develop a recommender system, which returns predictions for a user, based on a matrix factorization algorithm (Alternating Least Squares, ALS). It also returns similar items based on Jaccard similarity and euclidian distance. We conclude with system evaluation by using Precision@k statistical measure. The evaluation results have shown 19% precision accuracy, which greatly exceeds the results of random recommendation that achieves 1% precision accuracy. We also propose a potential website implementation with the intention of improving business results