Publicação
Tuning metadata for better movie content-based recommendation systems
| Resumo: | The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented. |
|---|---|
| Autores principais: | Márcio Micael Soares |
| Outros Autores: | Paula Viana |
| Ano: | 2015 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | INESC TEC |
| Idioma: | inglês |
| Origem: | INESC TEC |
| _version_ | 1868329918838865920 |
|---|---|
| author | Márcio Micael Soares |
| author2 | Paula Viana |
| author2_role | author |
| author_facet | Márcio Micael Soares Paula Viana |
| author_role | author |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Márcio Micael Soares\"},{\"Person.name\":\"Paula Viana\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Márcio Micael Soares Paula Viana |
| datacite.date.Accepted.fl_str_mv | 2015-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2017-12-14T14:17:54Z |
| datacite.date.embargoed.fl_str_mv | 2017-12-14T14:17:54Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.titles.title.fl_str_mv | Tuning metadata for better movie content-based recommendation systems |
| dc.creator.none.fl_str_mv | Márcio Micael Soares Paula Viana |
| dc.date.Accepted.fl_str_mv | 2015-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2017-12-14T14:17:54Z |
| dc.date.embargoed.fl_str_mv | 2017-12-14T14:17:54Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://repositorio.inesctec.pt/handle/123456789/4089 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.title.fl_str_mv | Tuning metadata for better movie content-based recommendation systems |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| id | inesctec_8e7b61aa7cb95763f08ebfdbac2c19e8 |
| identifier.url.fl_str_mv | http://repositorio.inesctec.pt/handle/123456789/4089 |
| instacron_str | inesctec |
| institution | INESC TEC |
| instname_str | INESC TEC |
| language | eng |
| network_acronym_str | inesctec |
| network_name_str | INESC TEC |
| oai_identifier_str | oai:repositorio.inesctec.pt:123456789/4089 |
| organization_str_mv | urn:organizationAcronym:inesctec |
| person_str_mv | Márcio Micael Soares Paula Viana |
| publishDate | 2015 |
| reponame_str | INESC TEC |
| repository_id_str | urn:repositoryAcronym:inesctec |
| service_str_mv | urn:repositoryAcronym:inesctec |
| spelling | engThe increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented.application/pdfengengTuning metadata for better movie content-based recommendation systemsMárcio Micael SoaresPaula VianaURLhttp://repositorio.inesctec.pt/handle/123456789/4089DOIhttp://dx.doi.org/10.1007/s11042-014-1950-12017-12-14T14:17:54Z2015-01-01T00:00:00Z2015http://purl.org/coar/access_right/c_abf2open access1061836 byteshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.inesctec.pt/bitstreams/27fa951f-a02c-46b8-8b8e-b2da127b21d0/downloadliteraturehttp://purl.org/coar/resource_type/c_6501journal article |
| spellingShingle | Tuning metadata for better movie content-based recommendation systems Márcio Micael Soares |
| status | SINGLETON |
| title | Tuning metadata for better movie content-based recommendation systems |
| title_full | Tuning metadata for better movie content-based recommendation systems |
| title_fullStr | Tuning metadata for better movie content-based recommendation systems |
| title_full_unstemmed | Tuning metadata for better movie content-based recommendation systems |
| title_short | Tuning metadata for better movie content-based recommendation systems |
| title_sort | Tuning metadata for better movie content-based recommendation systems |
| url | http://repositorio.inesctec.pt/handle/123456789/4089 |
| visible | 1 |