As as part of EU project EcoDots we built a recommender system for eco-friendly accommodations at the website ecobnb.com. The system leverages Prediction.IO platform and Elasticserach to offer recommendations as a service.

G. Slapnicar et al.: Recommender System as a Service based on the Alternating Least Squares Algorithm (IS 2015)

In this paper, we describe a production-ready recommender system as a service for recommending eco-friendly tourist accommodations. It offers two main features: (1) it returns personalized recommendations for a user by creating a latent factor model through matrix factorization (Alternating Least Squares algorithm, ALS) and (2) it returns accommodations that are similar to a given accommodation by calculating content-based similarity using the Jaccard coefficient and the Euclidian distance. The system is evaluated on the collected data by using cross-validation and Precision@k as a performance measure. It achieves 19% Precision@k for personalized recommendations to a user based on his past interactions with accommodations. This score far surpasses a random recommender implementation that achieves 1% Precision@k.