Ranking, recommendation, and retrieval systems are widely used in online platforms and
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …

With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …

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

Recommender systems can be characterized as software solutions that provide users with
convenient access to relevant content. Traditionally, recommender systems research …

In Recommender Systems, impressions are a relatively new type of information that records
all products previously shown to the users. They are also a complex source of information …

Even though offline evaluation is just an imperfect proxy of online performance–due to the
interactive nature of recommenders–it will probably remain the primary way of evaluation in …

Recommender systems can be characterized as software solutions that provide users
convenient access to relevant content. Traditionally, recommender systems research …

Reinforcement learning based recommender systems (RL-based RS) aim at learning a
good policy from a batch of collected data, by casting recommendations to multi-step …

We develop and evaluate neural architectures to model the user behavior in recommender
systems (RS) inspired by click models for Web search but going beyond standard click …

Understanding the evolution of online news communities is essential for designing more
effective news recommender systems. However, due to the lack of appropriate datasets and …