  Recommender systems can be characterized as software solutions that provide
users convenient access to relevant content. Traditionally, recommender systems
research predominantly focuses on developing machine learning algorithms that
aim to predict which content is relevant for individual users. In real-world
applications, however, optimizing the accuracy of such relevance predictions as
a single objective in many cases is not sufficient. Instead, multiple and often
competing objectives have to be considered, leading to a need for more research
in multi-objective recommender systems. We can differentiate between several
types of such competing goals, including (i) competing recommendation quality
objectives at the individual and aggregate level, (ii) competing objectives of
different involved stakeholders, (iii) long-term vs. short-term objectives,
(iv) objectives at the user interface level, and (v) system level objectives.
In this paper we review these types of multi-objective recommendation settings
and outline open challenges in this area.
