System-wide eﬀectiveness of active learning in collaborative ﬁltering
published: Aug. 4, 2011, recorded: July 2011, views: 4153
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The accuracy of a collaborative-ﬁltering system largely depends on two factors: the quality of the recommendation algorithm and the number and quality of the available product 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 specific techniques, which are defined as rating elicitation strategies, can be used to selectively choosing the items to be presented to the user for rating. In this paper we consider several rating elicitation strategies and we evaluate their system utility, i.e., how the overall behavior of the system changes when new ratings are added. We discuss the pros and cons of different strategies with respect to several metrics (MAE, precision, NDCG and coverage). It is shown that different strategies can improve different aspects of the recommendation quality.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !