How to Get the Recommender Out of the Lab?

A personalised system is a complex piece of software made of many interacting parts, from data ingestion to presenting the results to the users. A plethora of methods, tools, algorithms and approaches exist for each piece of such a system: many data and metadata processing methods, many user models, many filtering techniques, many accuracy metrics, many personalisation levels. . . In addition, a realworld recommender is a piece of an even larger and more complex environment over which there is little control: often it is part of a larger application introducing constraints for the design of the recommender, e.g. the data may not be in a suitable format, or the environment may impose some architectural or privacy constraints. This can make the task of building such a recommender system daunting. This chapter intends to be a guide to the design, implementation and evaluation of personalised systems. It will present the different aspects that must be studied before the design is even started, and how to avoid pitfalls, in a hands-on approach.