The main objective of a personalized recommender system is to filter and present (recommend) to the user the most appropriate items according to his preferences. In many Case Based Recommendation systems, this goal is achieved by using weighted similarity measures. Thus, weighting the features, i.e. describing the items to be recommended, is a key issue in such systems. In this paper, we propose a dynamic weighting scheme for a Case Based Recommendation Syste, which is based on statistics of data extracted from past sessions. The applications of these ideas to an interactive, Case-Based travel recommender system, called Dietorecs, that guides European travelers for their travel decision making processes, are described
A dynamic approach to feature weighting
Arslan, Bora;Ricci, Francesco;Mirzadeh, Nader;Venturini, Adriano
2002-01-01
Abstract
The main objective of a personalized recommender system is to filter and present (recommend) to the user the most appropriate items according to his preferences. In many Case Based Recommendation systems, this goal is achieved by using weighted similarity measures. Thus, weighting the features, i.e. describing the items to be recommended, is a key issue in such systems. In this paper, we propose a dynamic weighting scheme for a Case Based Recommendation Syste, which is based on statistics of data extracted from past sessions. The applications of these ideas to an interactive, Case-Based travel recommender system, called Dietorecs, that guides European travelers for their travel decision making processes, are describedI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
