Research on recommender systems (RSs) has traditionally focused on the design of
systems capable of suggesting items of interest for users. However, often the most important …

There is growing research interest in recommendation as a multi-stakeholder problem, one
where the interests of multiple parties should be taken into account. This category subsumes …

Existing recommendation algorithms mostly focus on optimizing traditional recommendation
measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-k …

With the exponential rise in the size of data being generated, personalization based on
recommender systems has become an important aspect of digital marketing strategy of E …

In the recommender systems field, it is increasingly recognized that focusing on accuracy
measures is limiting and misguided. Unsurprisingly, in recent years, the field has witnessed …

In research practice, recommender systems are typically evaluated on their ability to provide
items that satisfy the needs and interests of the end user. However, in many …

In the year 2019, the Recommender Systems Challenge deals with a real-world task from
the area of e-tourism for the first time, namely the recommendation of hotels in booking …

Recommender Systems are becoming an inherent part of today's Internet. They can be
found anywhere from e-commerce platforms (eBay, Amazon) to music or movie streaming …

Nowadays, recommender systems are employed across a diverse set of application
domains, not only supporting us in our decision making and choices but also helping us to …

Historically, the main criterion for a successful recommender system was the relevance of
the recommended items to the user. In other words, the only objective for the …