1 resultado para Latent class model
em Repositório do ISCTE - Instituto Universitário de Lisboa
Filtro por publicador
- Aberystwyth University Repository - Reino Unido (2)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (6)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (2)
- Aquatic Commons (3)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (6)
- Archive of European Integration (1)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (2)
- Aston University Research Archive (43)
- B-Digital - Universidade Fernando Pessoa - Portugal (1)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (18)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (24)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (2)
- Bioline International (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (31)
- Boston University Digital Common (5)
- Brock University, Canada (4)
- Bucknell University Digital Commons - Pensilvania - USA (2)
- Bulgarian Digital Mathematics Library at IMI-BAS (7)
- CaltechTHESIS (2)
- Cambridge University Engineering Department Publications Database (15)
- CentAUR: Central Archive University of Reading - UK (41)
- Central European University - Research Support Scheme (1)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (14)
- Collection Of Biostatistics Research Archive (11)
- CORA - Cork Open Research Archive - University College Cork - Ireland (3)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (2)
- Dalarna University College Electronic Archive (1)
- Deakin Research Online - Australia (69)
- Digital Commons - Michigan Tech (3)
- Digital Commons @ DU | University of Denver Research (1)
- Digital Commons at Florida International University (10)
- Digital Peer Publishing (9)
- DigitalCommons@The Texas Medical Center (14)
- Diposit Digital de la UB - Universidade de Barcelona (1)
- DRUM (Digital Repository at the University of Maryland) (2)
- Duke University (16)
- eResearch Archive - Queensland Department of Agriculture; Fisheries and Forestry (1)
- FUNDAJ - Fundação Joaquim Nabuco (1)
- Glasgow Theses Service (1)
- Greenwich Academic Literature Archive - UK (2)
- Helda - Digital Repository of University of Helsinki (6)
- Indian Institute of Science - Bangalore - Índia (55)
- Instituto Politécnico do Porto, Portugal (2)
- Instituto Superior de Psicologia Aplicada - Lisboa (1)
- Massachusetts Institute of Technology (11)
- National Center for Biotechnology Information - NCBI (21)
- Nottingham eTheses (3)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (4)
- Publishing Network for Geoscientific & Environmental Data (2)
- QSpace: Queen's University - Canada (1)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (47)
- Queensland University of Technology - ePrints Archive (210)
- Repositorio Académico de la Universidad Nacional de Costa Rica (1)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (3)
- Repositorio de la Universidad de Cuenca (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (5)
- Repositório do ISCTE - Instituto Universitário de Lisboa (1)
- Repositório Institucional da Universidade de Aveiro - Portugal (2)
- Repositorio Institucional de la Universidad de Málaga (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (46)
- Research Open Access Repository of the University of East London. (2)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- Savoirs UdeS : plateforme de diffusion de la production intellectuelle de l’Université de Sherbrooke - Canada (1)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (18)
- Universidade Complutense de Madrid (8)
- Universidade Federal do Pará (2)
- Universidade Federal do Rio Grande do Norte (UFRN) (2)
- Universitat de Girona, Spain (3)
- Université de Lausanne, Switzerland (2)
- Université de Montréal (3)
- Université de Montréal, Canada (16)
- Université Laval Mémoires et thèses électroniques (1)
- University of Canberra Research Repository - Australia (1)
- University of Michigan (25)
- University of Queensland eSpace - Australia (21)
- University of Washington (3)
- WestminsterResearch - UK (2)
- Worcester Research and Publications - Worcester Research and Publications - UK (1)
Resumo:
Matrix factorization (MF) has evolved as one of the better practice to handle sparse data in field of recommender systems. Funk singular value decomposition (SVD) is a variant of MF that exists as state-of-the-art method that enabled winning the Netflix prize competition. The method is widely used with modifications in present day research in field of recommender systems. With the potential of data points to grow at very high velocity, it is prudent to devise newer methods that can handle such data accurately as well as efficiently than Funk-SVD in the context of recommender system. In view of the growing data points, I propose a latent factor model that caters to both accuracy and efficiency by reducing the number of latent features of either users or items making it less complex than Funk-SVD, where latent features of both users and items are equal and often larger. A comprehensive empirical evaluation of accuracy on two publicly available, amazon and ml-100 k datasets reveals the comparable accuracy and lesser complexity of proposed methods than Funk-SVD.