The comprehensive evaluation of the performance of a recommender system is a complex
endeavor: many facets need to be considered in configuring an adequate and effective …

Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …

We introduce the concept of expected exposure as the average attention ranked items
receive from users over repeated samples of the same query. Furthermore, we advocate for …

Information access systems, such as search and recommender systems, often use ranked
lists to present results believed to be relevant to the user's information need. Evaluating …

Collaborative filtering algorithms find useful patterns in rating and consumption data and
exploit these patterns to guide users to good items. Many of the patterns in rating datasets …

Recommender systems research and practice are fast-developing topics with growing
adoption in a wide variety of information access scenarios. In this article, we present an …

Personalized recommender systems have become indispensable in today's online world.
Most of today's recommendation algorithms are data-driven and based on behavioral data …

Current practice for evaluating recommender systems typically focuses on point estimates of
user-oriented effectiveness metrics or business metrics, sometimes combined with …

Abstract Point-of-interest (POI) recommendation is an essential service to location-based
social networks (LBSNs), benefiting both users providing them the chance to explore new …

As global warming soars, the need to assess the environmental impact of research is
becoming increasingly urgent. Despite this, few recommender systems research papers …