  Recommender systems are used in many different applications and contexts,
however their main goal can always be summarised as "connecting relevant
content to interested users". Personalized recommendation algorithms achieve
this goal by first building a profile of the user, either implicitly or
explicitly, and then matching items with this profile to find relevant content.
The more interpretable the profile and this "matching function" are, the easier
it is to provide users with accurate and intuitive explanations, and also to
let them interact with the system. Indeed, for a user to see what the system
has already learned about her interests is of key importance for her to provide
feedback to the system and to guide it towards better understanding her
preferences.
  To this end, we propose a linear collaborative filtering recommendation model
that builds user profiles within the domain of item metadata, which is arguably
the most interpretable domain for end users. Our method is hence inherently
transparent and explainable. Moreover, since recommendations are computed as a
linear function of item metadata and the interpretable user profile, our method
seamlessly supports interactive recommendation. In other words, users can
directly tweak the weights of the learned profile for more fine-grained
browsing and discovery of content based on their current interests.
  We demonstrate the interactive aspect of this model in an online application
for discovering cultural events in Belgium. Additionally, the performance of
the model is evaluated with offline experiments, both static and with simulated
feedback, and compared to several state-of-the-art and state-of-practice
baselines.
