Recommender systems are decision support tools aimed at assisting users in finding products that best suit their preferences. The success of a recommendation session depends significantly on how, at the beginning of the interaction, the system initializes its representation of user's preferences. In mobile recommender systems, guessing an initial represen- tation of user's preferences is even more di±cult because of some limitations of mobile devices as well as characteristics of mobile users. In this paper we propose an approach for user preferences initialization that exploits a range of avail- able knowledge sources related to the user. In this approach personalized recommendations can be generated using both a persistent and a context-dependent user model
User Preferences Initialization and Integration in CritiqueBased Mobile Recommender Systems
Ricci, Francesco
2004-01-01
Abstract
Recommender systems are decision support tools aimed at assisting users in finding products that best suit their preferences. The success of a recommendation session depends significantly on how, at the beginning of the interaction, the system initializes its representation of user's preferences. In mobile recommender systems, guessing an initial represen- tation of user's preferences is even more di±cult because of some limitations of mobile devices as well as characteristics of mobile users. In this paper we propose an approach for user preferences initialization that exploits a range of avail- able knowledge sources related to the user. In this approach personalized recommendations can be generated using both a persistent and a context-dependent user modelI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
