Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

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 …

In recommender systems, top-N recommendation is an important task with implicit feedback
data. Although the recent success of deep learning largely pushes forward the research on …

Recommender models are hard to evaluate, particularly under offline setting. In this article,
we provide a comprehensive and critical analysis of the data leakage issue in recommender …

Advanced music recommendation systems are being introduced along with the
development of machine learning. However, it is essential to design a music …

We investigate the impact of popularity bias in false-positive metrics in the offline evaluation
of recommender systems. Unlike their true-positive complements, false-positive metrics …

As a result of technological advancements, the number of IoT devices and services is rapidly
increasing. Due to the increasing complexity of IoT devices and the various ways they can …

Next-item recommender systems are often trained using only positive feedback with
randomly-sampled negative feedback. We show the benefits of using real negative feedback …

We propose a simple model of an idealized online cultural market in which N items,
endowed with a hidden quality metric, are recommended to users by a ranking algorithm …

The term “Natural Design” has various meanings and applications within and beyond the
human-computer interaction community. Yet, there is no consensus on whether it is a …