1 resultado para Recommender System, Opinion Mining, Association Rule Mining, User Review
em Universidade Federal de Uberlândia
Filtro por publicador
- Aberdeen University (1)
- Academic Archive On-line (Stockholm University; Sweden) (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (5)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (15)
- Aquatic Commons (1)
- Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer (1)
- Archive of European Integration (19)
- Aston University Research Archive (27)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (3)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (13)
- Biblioteca Virtual del Sistema Sanitario Público de Andalucía (BV-SSPA), Junta de Andalucía. Consejería de Salud y Bienestar Social, Spain (2)
- Bioline International (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (12)
- Brock University, Canada (3)
- Bulgarian Digital Mathematics Library at IMI-BAS (10)
- CentAUR: Central Archive University of Reading - UK (143)
- CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal (2)
- Cochin University of Science & Technology (CUSAT), India (10)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (2)
- Consorci de Serveis Universitaris de Catalunya (CSUC), Spain (23)
- Cor-Ciencia - Acuerdo de Bibliotecas Universitarias de Córdoba (ABUC), Argentina (1)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (1)
- CUNY Academic Works (2)
- Dalarna University College Electronic Archive (5)
- Department of Computer Science E-Repository - King's College London, Strand, London (2)
- Digital Commons - Michigan Tech (3)
- Digital Commons - Montana Tech (1)
- Digital Commons @ Winthrop University (1)
- Digital Commons at Florida International University (10)
- Digital Peer Publishing (3)
- DigitalCommons@The Texas Medical Center (2)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (41)
- DRUM (Digital Repository at the University of Maryland) (4)
- Duke University (1)
- Gallica, Bibliotheque Numerique - Bibliothèque nationale de France (French National Library) (BnF), France (1)
- Harvard University (1)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (2)
- Instituto Politécnico de Viseu (1)
- Instituto Politécnico do Porto, Portugal (40)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (2)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (1)
- Martin Luther Universitat Halle Wittenberg, Germany (10)
- Massachusetts Institute of Technology (3)
- Nottingham eTheses (7)
- Open University Netherlands (1)
- Portal do Conhecimento - Ministerio do Ensino Superior Ciencia e Inovacao, Cape Verde (2)
- QSpace: Queen's University - Canada (2)
- RDBU - Repositório Digital da Biblioteca da Unisinos (3)
- ReCiL - Repositório Científico Lusófona - Grupo Lusófona, Portugal (1)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (12)
- Repositório da Produção Científica e Intelectual da Unicamp (2)
- Repositorio de la Universidad de Cuenca (1)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (2)
- Repositório do ISCTE - Instituto Universitário de Lisboa (1)
- Repositório Institucional da Universidade de Aveiro - Portugal (2)
- Repositório Institucional da Universidade de Brasília (2)
- Repositório Institucional da Universidade Tecnológica Federal do Paraná (RIUT) (1)
- Repositorio Institucional de la Universidad de Málaga (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (22)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (21)
- School of Medicine, Washington University, United States (2)
- Scielo Saúde Pública - SP (32)
- Scielo Uruguai (1)
- Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom (1)
- South Carolina State Documents Depository (1)
- Universidad Autónoma de Nuevo León, Mexico (2)
- Universidad de Alicante (21)
- Universidad del Rosario, Colombia (9)
- Universidad Politécnica de Madrid (27)
- Universidade de Madeira (1)
- Universidade do Minho (29)
- Universidade Federal de Uberlândia (1)
- Universidade Federal do Pará (1)
- Universidade Metodista de São Paulo (3)
- Universitat de Girona, Spain (9)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (15)
- Université de Lausanne, Switzerland (35)
- Université de Montréal (1)
- Université de Montréal, Canada (9)
- Université Laval Mémoires et thèses électroniques (1)
- University of Michigan (12)
- University of Queensland eSpace - Australia (60)
- University of Southampton, United Kingdom (7)
- University of Washington (1)
Resumo:
Nowadays, the amount of customers using sites for shopping is greatly increasing, mainly due to the easiness and rapidity of this way of consumption. The sites, differently from physical stores, can make anything available to customers. In this context, Recommender Systems (RS) have become indispensable to help consumers to find products that may possibly pleasant or be useful to them. These systems often use techniques of Collaborating Filtering (CF), whose main underlying idea is that products are recommended to a given user based on purchase information and evaluations of past, by a group of users similar to the user who is requesting recommendation. One of the main challenges faced by such a technique is the need of the user to provide some information about her preferences on products in order to get further recommendations from the system. When there are items that do not have ratings or that possess quite few ratings available, the recommender system performs poorly. This problem is known as new item cold-start. In this paper, we propose to investigate in what extent information on visual attention can help to produce more accurate recommendation models. We present a new CF strategy, called IKB-MS, that uses visual attention to characterize images and alleviate the new item cold-start problem. In order to validate this strategy, we created a clothing image database and we use three algorithms well known for the extraction of visual attention these images. An extensive set of experiments shows that our approach is efficient and outperforms state-of-the-art CF RS.