In this paper we investigate the relationship between a folksonomy-based music classification and a music classification based on collaborative filtering, i.e. on the users' listening behavior. We found a correlation between folksonomy-based songs clustering and clustering computed using methods based on the audience listening behaviour and, using a combination of the two approaches, we also computed the eclecticism level of a sample set of users, finding that eclecticism seems to be a characteristic which changes according to the genre of music most loved by a user
Recommending the most appropriate music is one of the most studied fields in the context of Recommen...
Research on cultural consumption typically identifies different cultural patterns which allow resear...
[[abstract]]In this paper, we propose a new rating-based collaborative music recommendation approach...
In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information o...
User models that capture the musical preferences of users are central for many tasks in music inform...
The next generation of music recommendation systems will be increasingly intelligent and likely take...
This thesis presents a new approach to recommend suitable tracks from a collection of songs to the u...
A shortcoming of current approaches for music recommen-dation is that they consider user-specific ch...
Nowadays, advanced information and communication technologies ease the access of music pieces. Howev...
This article reflects on the use of predetermined genre lists to measure patterns in music taste and...
Abstract. We investigate a range of music recommendation algorithm combinations, score aggregation f...
We investigate a range of music recommendation algorithm combinations, score aggregation functions, ...
Over the past century, sociocultural and technological developments have fostered the emergence of w...
Over the past century, sociocultural and technological developments have fostered the emergence of w...
Presentat a Machine Learning for Media Discovery Workshop, celebrat dins The 37th International Conf...
Recommending the most appropriate music is one of the most studied fields in the context of Recommen...
Research on cultural consumption typically identifies different cultural patterns which allow resear...
[[abstract]]In this paper, we propose a new rating-based collaborative music recommendation approach...
In this paper, we describe the LFM-1b User Genre Profile dataset. It provides detailed information o...
User models that capture the musical preferences of users are central for many tasks in music inform...
The next generation of music recommendation systems will be increasingly intelligent and likely take...
This thesis presents a new approach to recommend suitable tracks from a collection of songs to the u...
A shortcoming of current approaches for music recommen-dation is that they consider user-specific ch...
Nowadays, advanced information and communication technologies ease the access of music pieces. Howev...
This article reflects on the use of predetermined genre lists to measure patterns in music taste and...
Abstract. We investigate a range of music recommendation algorithm combinations, score aggregation f...
We investigate a range of music recommendation algorithm combinations, score aggregation functions, ...
Over the past century, sociocultural and technological developments have fostered the emergence of w...
Over the past century, sociocultural and technological developments have fostered the emergence of w...
Presentat a Machine Learning for Media Discovery Workshop, celebrat dins The 37th International Conf...
Recommending the most appropriate music is one of the most studied fields in the context of Recommen...
Research on cultural consumption typically identifies different cultural patterns which allow resear...
[[abstract]]In this paper, we propose a new rating-based collaborative music recommendation approach...