Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings

Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. However, novel and diverse suggestions also contribute to...

Descripción completa

Detalles Bibliográficos
Autores principales: Landin, Alfonso, Parapar, Javier, Barreiro, Álvaro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148017/
http://dx.doi.org/10.1007/978-3-030-45442-5_27
_version_ 1783520512401473536
author Landin, Alfonso
Parapar, Javier
Barreiro, Álvaro
author_facet Landin, Alfonso
Parapar, Javier
Barreiro, Álvaro
author_sort Landin, Alfonso
collection PubMed
description Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. However, novel and diverse suggestions also contribute to user satisfaction. Unfortunately, it is common to harm those two aspects when optimizing recommendation accuracy. In this paper, we present EER, a linear model for the top-N recommendation task, which takes advantage of user and item embeddings for improving novelty and diversity without harming accuracy.
format Online
Article
Text
id pubmed-7148017
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-71480172020-04-13 Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings Landin, Alfonso Parapar, Javier Barreiro, Álvaro Advances in Information Retrieval Article Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. However, novel and diverse suggestions also contribute to user satisfaction. Unfortunately, it is common to harm those two aspects when optimizing recommendation accuracy. In this paper, we present EER, a linear model for the top-N recommendation task, which takes advantage of user and item embeddings for improving novelty and diversity without harming accuracy. 2020-03-24 /pmc/articles/PMC7148017/ http://dx.doi.org/10.1007/978-3-030-45442-5_27 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Landin, Alfonso
Parapar, Javier
Barreiro, Álvaro
Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings
title Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings
title_full Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings
title_fullStr Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings
title_full_unstemmed Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings
title_short Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings
title_sort novel and diverse recommendations by leveraging linear models with user and item embeddings
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148017/
http://dx.doi.org/10.1007/978-3-030-45442-5_27
work_keys_str_mv AT landinalfonso novelanddiverserecommendationsbyleveraginglinearmodelswithuseranditemembeddings
AT paraparjavier novelanddiverserecommendationsbyleveraginglinearmodelswithuseranditemembeddings
AT barreiroalvaro novelanddiverserecommendationsbyleveraginglinearmodelswithuseranditemembeddings

Ejemplares similares