1,494 research outputs found
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
Popularity Bias in Recommendation: A Multi-stakeholder Perspective
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
Recommender systems and their ethical challenges
This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system
Recommender systems fairness evaluation via generalized cross entropy
Fairness in recommender systems has been considered with respect
to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue
in a multistakeholder setting). Regardless, the concept has been
commonly interpreted as some form of equality – i.e., the degree to
which the system is meeting the information needs of all its users in
an equal sense. In this paper, we argue that fairness in recommender
systems does not necessarily imply equality, but instead it should
consider a distribution of resources based on merits and needs.We
present a probabilistic framework based ongeneralized cross entropy
to evaluate fairness of recommender systems under this perspective,
wherewe showthat the proposed framework is flexible and explanatory
by allowing to incorporate domain knowledge (through an ideal
fair distribution) that can help to understand which item or user aspects
a recommendation algorithm is over- or under-representing.
Results on two real-world datasets show the merits of the proposed
evaluation framework both in terms of user and item fairnessThis work was supported in part by the Center for Intelligent Information
Retrieval and in part by project TIN2016-80630-P (MINECO
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