Abstract
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.
Original language | English |
---|---|
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
Conference | 7th International Workshop on Personalized Access, Profile Management, and Context Awareness in Databases, PersDB 2013 |
---|---|
Country/Territory | Italy |
City | Riva del Garda, Trento |
Period | 30/08/13 → 30/08/13 |
All Science Journal Classification (ASJC) codes
- Software