Recommender systems are ubiquitous. Netflix recommends movies and tv series to users. Spotify recommends audio tracks. Facebook recommends friends. Although these recommendations are often precise, they are also often boring. For example, these systems often suggest items, such as movies or audio recordings that users are already familiar with or would find anyway by themselves. To mitigate this problem, researchers started optimising recommender systems not only for accuracy (items that users like), but also for serendipity. There is no consensus on the definition of the term serendipity in recommender systems (RecSys), but most often researchers consider an item serendipitous if it is relevant, novel and unexpected to the user. Relevance indicates that the user is interested in the item, novelty - the user had been unaware of the item prior to consuming it, while unexpectedness has a number of definitions with the most common being that the item is dissimilar to what the user usually consumes.
