Healthy Food Recommendation and Explanation Generation using a Semantically-Enabled Framework?

Abstract

Recommender systems are an important service utilized in a wide variety of applications, but they rarely explain the processes and data used to generate the final recommendation. Furthermore, no con-venient software resource exists to help developers create recommender systems that use a variety of strategies, such as knowledge-driven rec-ommendation processes. We present a Python framework to support the development of recommender systems with an emphasis on using data sources containing rich semantics and providing explanations for each step involved in producing the recommendation. We illustrate this through an example food recommendation system developed using the framework.