Traditionally, especially in academic research in recommender systems, the
focus has been solely on the satisfaction of the end-user. While user
satisfaction has, indeed, been associated with the success of the business, it
is not the only factor. In many recommendation domains, there are other
stakeholders whose needs should be taken into account in the recommendation
generation and evaluation. In this dissertation, I describe the notion of
multi-stakeholder recommendation. In particular, I study one of the most
important challenges in recommendation research, popularity bias, from a
multi-stakeholder perspective since, as I show later in this dissertation, it
impacts different stakeholders in a recommender system. Popularity bias is a
well-known phenomenon in recommender systems where popular items are
recommended even more frequently than their popularity would warrant,
amplifying long-tail effects already present in many recommendation domains.
Prior research has examined various approaches for mitigating popularity bias
and enhancing the recommendation of long-tail items overall. The effectiveness
of these approaches, however, has not been assessed in multi-stakeholder
environments. In this dissertation, I study the impact of popularity bias in
recommender systems from a multi-stakeholder perspective. In addition, I
propose several algorithms each approaching the popularity bias mitigation from
a different angle and compare their performances using several metrics with
some other state-of-the-art approaches in the literature. I show that, often,
the standard evaluation measures of popularity bias mitigation in the
literature do not reflect the real picture of an algorithm's performance when
it is evaluated from a multi-stakeholder point of view.Comment: PhD Dissertation in Information Science (University of Colorado
Boulder
