Tourism is an important application domain for recommender systems. In this domain,
recommender systems are for example tasked with providing personalized …

Recommender systems have shown to be valuable tools for filtering, ranking, and discovery
in a variety of application domains such as e-commerce, media repositories or document …

Abstract Recommender or Recommendation Systems (RS) aim to help users dealing with
information overload: finding relevant items in a vast space of resources. Research on RS …

One common dichotomy faced in recommender systems is that explicit user feedback-in the
form of ratings, tags, or user-provided personal information-is scarce, yet the most popular …

Personalization of playlists is a common feature in music streaming services, but
conventional techniques, such as collaborative filtering, rely on explicit assumptions …

Most of the approaches for understanding user preferences or taste are based on having
explicit feedback from users. However, in many real-life situations we need to rely on implicit …

Recommender systems are software tools to tackle the problem of information overload by
helping users to find items that are most relevant for them within an often unmanageable set …

Most collaborative filtering models assume that the interaction of users with items take a
single form, eg, only ratings or clicks or views. In fact, in most real-life recommendation …

Modeling users' online behavior has great benefit for many e-Commerce web sites and
search engines. In the context of software service selection, if we could understand users' …

Many studies have sought to understand the behavior of music listeners to design an
improved music listening experience. This is especially important in music recommendation …