A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
Lecture Notes in Business Information Processing
The accuracy of collaborative filtering recommender systems largely depends on two factors: the quality of the recommendation algorithm and the nature of the available item ratings. In general, the more ratings are elicited from the users, the more effective the recommendations are. However, not all the ratings are equally useful and therefore, in order to minimize the users' rating effort, only some of them should be requested or acquired. In this paper we consider several rating elicitationdoi:10.1007/978-3-642-23014-1_14 fatcat:fzqzdxvyabhozlcoy5m3cwvm2e