Active Learning to Reduce Cold Start in Recommender Systems

Every time a recommender system has a new user, it does not have enough information to generate recommendations with high precision, this is known as cold start. Adapting this problem to a classification problem allow us to apply Active Learning techniques that, as we well see, offer some methods to...

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Detalles Bibliográficos
Autor principal: Silvi, Luciano
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2017
Materias:
Ciencias Informáticas
recommender systems
active learning
cold start
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/63482
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-63482
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
recommender systems
active learning
cold start
spellingShingle Ciencias Informáticas
recommender systems
active learning
cold start
Silvi, Luciano
Active Learning to Reduce Cold Start in Recommender Systems
topic_facet Ciencias Informáticas
recommender systems
active learning
cold start
description Every time a recommender system has a new user, it does not have enough information to generate recommendations with high precision, this is known as cold start. Adapting this problem to a classification problem allow us to apply Active Learning techniques that, as we well see, offer some methods to, given the less possible information about a new user, make right predictions with higher precision than the standard solutions applied in this situation.
format Objeto de conferencia
Objeto de conferencia
author Silvi, Luciano
author_facet Silvi, Luciano
author_sort Silvi, Luciano
title Active Learning to Reduce Cold Start in Recommender Systems
title_short Active Learning to Reduce Cold Start in Recommender Systems
title_full Active Learning to Reduce Cold Start in Recommender Systems
title_fullStr Active Learning to Reduce Cold Start in Recommender Systems
title_full_unstemmed Active Learning to Reduce Cold Start in Recommender Systems
title_sort active learning to reduce cold start in recommender systems
publishDate 2017
url http://sedici.unlp.edu.ar/handle/10915/63482
work_keys_str_mv AT silviluciano activelearningtoreducecoldstartinrecommendersystems
bdutipo_str Repositorios
_version_ 1764820480837025795

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