Recommender systems (RS) rely on interaction data between users and items to generate effective results. Historically, RS aimed to deliver the most consistent (i.e., accurate) items to the trained user profiles. However, the attention towards additional (...

In the context of recommender systems (RS), the concept of diversity is probably the most studied perspective beyond mere accuracy. Despite the extensive development of diversity measures and enhancement methods, the understanding of how users perceive ...

Improvements in the recommender systems (RS) domain are not possible without a thorough way to evaluate and compare newly proposed approaches. User studies represent a viable alternative to online and offline evaluation schemes, but despite their ...

Multi-objective recommender systems (MORS) provide suggestions to users according to multiple (and possibly conflicting) goals. When a system optimizes its results at the individual-user level, it tailors them on a user’s propensity towards the different ...

Going beyond accuracy in the evaluation of a recommender system is an aspect that is receiving more and more attention. Among the many perspectives that can be considered, the impact of presentation bias is of central importance. Under presentation bias, ...

Group recommender systems (GRS) are a specific case of recommender systems (RS), where recommendations are constructed to a group of users rather than an individual. GRS has diverse application areas including trip planning, recommending movies to watch ...

Nowadays, deep learning based models like CLIP allow simple design of cross-modal video search systems that are able to solve many tasks considered as highly challenging several years ago. In this paper, we analyze a CLIP based search approach ...

Group recommender systems (GRS) focus on recommending items to groups of users. GRS need to tackle the heterogeneity of group members’ preferences and produce recommendations of high overall utility while also considering some sense of fairness among ...

The main focus of our work is the problem of multiple objectives optimization (MOO) while providing a final list of recommendations to the user. Currently, system designers can tune MOO by setting importance of individual objectives, usually in some ...

Group recommendations are a specific case of recommender systems (RS), where instead of recommending for each individual independently, shared recommendations are produced for groups of users. Usually, group recommendation techniques (i.e., group ...

Interactive video search systems effectively combine text-image embedding approaches and smart user interfaces allowing various means of browsing in intermediate result sets. In this paper, we combine features from VIRET and SOMHunter systems into ...