The paper presents our participation [5] at the ECML/PKDD 2011 - Discovery challenge for the task on the cold start problem. The challenge dataset was gathered from VideoLectures.Net web site that exploits a Recommender System (RS) to guide users during the access to its large multimedia repository of video lectures. Cold start concerns performance issues when new items and new users should be handled by a RS and it is commonly associated with pure collaborative ltering- based RSs. The proposed approach exploits the challenge data to predict the frequencies of pairs of cold items and old items and then the highest values are used to provide recommendations.
Cold Start Problem: a Lightweight Approach at ECML/PKDD 2011 - Discovery Challenge
SEMERARO, Giovanni
2012-01-01
Abstract
The paper presents our participation [5] at the ECML/PKDD 2011 - Discovery challenge for the task on the cold start problem. The challenge dataset was gathered from VideoLectures.Net web site that exploits a Recommender System (RS) to guide users during the access to its large multimedia repository of video lectures. Cold start concerns performance issues when new items and new users should be handled by a RS and it is commonly associated with pure collaborative ltering- based RSs. The proposed approach exploits the challenge data to predict the frequencies of pairs of cold items and old items and then the highest values are used to provide recommendations.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
