  Traditionally, recommendation algorithms have been designed for service
developers. However, recently, a new paradigm called user-side recommender
systems has been proposed and they enable web service users to construct their
own recommender systems without access to trade-secret data. This approach
opens the door to user-defined fair systems even if the official recommender
system of the service is not fair. While existing methods for user-side
recommender systems have addressed the challenging problem of building
recommender systems without using log data, they rely on heuristic approaches,
and it is still unclear whether constructing user-side recommender systems is a
well-defined problem from theoretical point of view. In this paper, we provide
theoretical justification of user-side recommender systems. Specifically, we
see that hidden item features can be recovered from the information available
to the user, making the construction of user-side recommender system
well-defined. However, this theoretically grounded approach is not efficient.
To realize practical yet theoretically sound recommender systems, we propose
three desirable properties of user-side recommender systems and propose an
effective and efficient user-side recommender system, \textsc{Consul}, based on
these foundations. We prove that \textsc{Consul} satisfies all three
properties, whereas existing user-side recommender systems lack at least one of
them. In the experiments, we empirically validate the theory of feature
recovery via numerical experiments. We also show that our proposed method
achieves an excellent trade-off between effectiveness and efficiency and
demonstrate via case studies that the proposed method can retrieve information
that the provider's official recommender system cannot.
