Recommender systems usually rely on user profiles to generate personalized recommendations. We argue here that such profiles are often too coarse to capture the current user's state of mind/desire. For example, a serious user that usually prefers documentary features may, at the end of a long and tiring conference, be in the mood for a lighter entertaining movie, not captured by her usual profile. As communicating one's state of mind to a system in (key)words may be difficult, we propose in this work an alternative method which allows users to describe their current desire/mood through examples. Our algorithms utilizes the user's examples to refine the recommendations generated by a given system, considering several, possibly competing, desired properties of the recommended items set (rating, similarity, diversity, coverage). The algorithms are based on a simple geometric representation of the example items, which allows for efficient processing and the generation of suitable recommendations even in the absence of semantic information.
|State||Published - 2013|
|Event||7th International Workshop on Personalized Access, Profile Management, and Context Awareness in Databases, PersDB 2013 - Riva del Garda, Trento, Italy|
Duration: 30 Aug 2013 → 30 Aug 2013
|Conference||7th International Workshop on Personalized Access, Profile Management, and Context Awareness in Databases, PersDB 2013|
|City||Riva del Garda, Trento|
|Period||30/08/13 → 30/08/13|