The age of machine learning and data analytics have changed the habits of entertainment. Recommendation systems have been improving in the last years, with relevant commercial purposes, and many top-level companies –such as Amazon...
ver más
¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video
Proyectos interesantes
TIN2012-32682
AUMENTO DE PRESTACIONES EN LOS SISTEMAS DE RECOMENDACION BAS...
14K€
Cerrado
TIN2014-55006-R
PERSONALIZACION SOCIAL EN SISTEMAS DE RECOMENDACION
81K€
Cerrado
PID2019-108965GB-I00
MAS ALLA DE LA RECOMENDACION ESTATICA: EQUIDAD, INTERACCION...
99K€
Cerrado
MUSICAL-MOODS
A mood indexed database of scores lyrics musical excerpts...
244K€
Cerrado
TIN2016-80630-P
RECOMENDACION EN MEDIOS SOCIALES: CONTEXTO, DIVERSIDAD Y SES...
82K€
Cerrado
Duración del proyecto: 25 meses
Fecha Inicio: 2021-03-17
Fecha Fin: 2023-04-30
Líder del proyecto
IE UNIVERSIDAD
No se ha especificado una descripción o un objeto social para esta compañía.
Sin perfil tecnológico
Presupuesto del proyecto
150K€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
The age of machine learning and data analytics have changed the habits of entertainment. Recommendation systems have been improving in the last years, with relevant commercial purposes, and many top-level companies –such as Amazon, Google or Netflix- are investing high amounts of money in improving their algorithms based on Artificial Intelligence. The case of music has been especially relevant, as the market has drastically changed in the last 10 years, moving towards a user-centric streaming model, where user preferences make the difference and dynamic playlists are the key of streaming success. Recommenders are built based on three main strategies:
1) similarities between songs that are identified by their soundwaves;
2) classification using conventional tags for songs, such as author, genre, period or, in some cases, mood; and
3) collaborative tagging by users.
In this context, song lyrics (the text of songs) are barely considered for the improvement of these strategies. Moreover, recommendations based on lyrics are done by hand with uneven criteria and filters. This Proof of Concept proposes the creation of an AI based recommendation engine (i.e. web service API) for analyzing song lyrics using POSTDATA ERC Project algorithms as its technical scaffold. Natural Language Processing tools for poetry analysis will be used to build a web service API to process lyrics and extract knowledge as additional metadata to enrich the companies´recommender systems. This approach will open an exciting opportunity to contribute to boosting the music entertainment world using artificial intelligence and language technologies.