We introduce an application combining CBR and collaborative filtering techniques in the music domain. We describe a scenario in which the classical collaborative filtering recommendation algorithm suffers from serious drawbacks: this scenario stresses the difference between a single-interaction case and a dynamically growing user profile. We set up a framework meant to extend collaborative filtering for compositional recommendation systems where cases does not explicitly yield the amount of overlapping items needed by classical filterin
Recommending media objects to users typically requires users to rate existing media objects so as to...
As one of the most successful approaches to building recommender systems, collaborative filtering (C...
Nowadays, advanced information and communication technologies ease the access of music pieces. Howev...
We introduce an application combining CBR and collaborative filtering techniques in the music domain...
We introduce an application combining CBR and collaborative filtering techniques in the music domain...
We introduce an application combining CBR and collaborative filtering techniques in the music domain...
Recommendation of music is emerging with force nowadays due to the huge amount of music content and ...
In this paper 1 we give an overview of the RACOFI (Rule-Applying Collaborative Filtering) multidimen...
Although content is fundamental to our music listening preferences, the leading performance in music...
Many collaborative music recommender systems (CMRS) have succeeded in capturing the similarity among...
Internet and E-commerce are the generators of abundant of data, causing information Overloading. Th...
In the context of PTV, an applied recommender system operating in the TV listings domain, we are exa...
To incorporate content information into collab-orative filtering methods, we train a neural net-work...
Abstract—Recommender Systems apply machine learning and data mining techniques to filter undetected ...
Typically, case-based recommender systems recommend single items to the on-line customer. In this pa...
Recommending media objects to users typically requires users to rate existing media objects so as to...
As one of the most successful approaches to building recommender systems, collaborative filtering (C...
Nowadays, advanced information and communication technologies ease the access of music pieces. Howev...
We introduce an application combining CBR and collaborative filtering techniques in the music domain...
We introduce an application combining CBR and collaborative filtering techniques in the music domain...
We introduce an application combining CBR and collaborative filtering techniques in the music domain...
Recommendation of music is emerging with force nowadays due to the huge amount of music content and ...
In this paper 1 we give an overview of the RACOFI (Rule-Applying Collaborative Filtering) multidimen...
Although content is fundamental to our music listening preferences, the leading performance in music...
Many collaborative music recommender systems (CMRS) have succeeded in capturing the similarity among...
Internet and E-commerce are the generators of abundant of data, causing information Overloading. Th...
In the context of PTV, an applied recommender system operating in the TV listings domain, we are exa...
To incorporate content information into collab-orative filtering methods, we train a neural net-work...
Abstract—Recommender Systems apply machine learning and data mining techniques to filter undetected ...
Typically, case-based recommender systems recommend single items to the on-line customer. In this pa...
Recommending media objects to users typically requires users to rate existing media objects so as to...
As one of the most successful approaches to building recommender systems, collaborative filtering (C...
Nowadays, advanced information and communication technologies ease the access of music pieces. Howev...