Creating recommendation systems is a tricky task. We need to add the temporal domain to the data. In particular, we need to make sure that we capture what was recommended before to the specific user and how the user reacted upon that. We also need to capture the evolution of the users and the data.
In this paper, the authors present a framework, RectoLibry, which helps to construct these kind of systems. The system captures both the parts of the development of the recommendations, but also their deployment.
The system is based on designing an ontology (yes, my old, good friend, used since before Web 2.0, even in my own research: https://link.springer.com/chapter/10.1007/978-3-540-87875-9_60 , https://link.springer.com/chapter/10.1007/3-540-46102-7_20 ).
The ontology describes the relationships existing in the recommendation domain and provide the support for the selections and feedback loops.
I recommend to take a look at the paper and the framework if you want to build a recommendation system. I will, when looking at the assignments from the software measurement PhD course.
