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 …

The design of algorithms that generate personalized ranked item lists is a central topic of
research in the field of recommender systems. In the past few years, in particular …

Recommender systems are the algorithms which select, filter, and personalize content
across many of the world's largest platforms and apps. As such, their positive and negative …

Effective methodologies for evaluating recommender systems are critical, so that different
systems can be compared in a sound manner. A commonly overlooked aspect of evaluating …

Offline evaluation is a popular approach to determine the best algorithm in terms of the
chosen quality metric. However, if the chosen metric calculates something unexpected, this …

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 …

Offline evaluation is an essential complement to online experiments in the selection,
improvement, tuning, and deployment of recommender systems. Offline methodologies for …

Music Recommender Systems (mRS) are designed to give personalised and meaningful
recommendations of items (ie songs, playlists or artists) to a user base, thereby reflecting …

Approaches to recommendation are typically evaluated in one of two ways:(1) via a
(simulated) online experiment, often seen as the gold standard, or (2) via some offline …