5,028 research outputs found
Incorporating System-Level Objectives into Recommender Systems
One of the most essential parts of any recommender system is
personalization-- how acceptable the recommendations are from the user's
perspective. However, in many real-world applications, there are other
stakeholders whose needs and interests should be taken into account. In this
work, we define the problem of multistakeholder recommendation and we focus on
finding algorithms for a special case where the recommender system itself is
also a stakeholder. In addition, we will explore the idea of incremental
incorporation of system-level objectives into recommender systems over time to
tackle the existing problems in the optimization techniques which only look for
optimizing the individual users' lists.Comment: arXiv admin note: text overlap with arXiv:1901.0755
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
Content-boosted Matrix Factorization Techniques for Recommender Systems
Many businesses are using recommender systems for marketing outreach.
Recommendation algorithms can be either based on content or driven by
collaborative filtering. We study different ways to incorporate content
information directly into the matrix factorization approach of collaborative
filtering. These content-boosted matrix factorization algorithms not only
improve recommendation accuracy, but also provide useful insights about the
contents, as well as make recommendations more easily interpretable
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