An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems
Authors:
- Manizheh RanjbarMRManizheh Ranjbar
- Parham MoradiPMParham Moradi
- Mostafa AzamiMAMostafa Azami
- Mahdi JaliliMJMahdi Jalili
- 1 November 2015
- journal article
- Published by Elsevier in Engineering Applications of Artificial Intelligence
- Vol. 46, 58-66
Abstract
No abstract availableFunding Information
- Australian Research Council (DE140100620)
This publication has 43 references indexed in Scilit:
- Incremental Collaborative Filtering recommender based on Regularized Matrix FactorizationKnowledge-Based Systems, 2012
- A new collaborative filtering metric that improves the behavior of recommender systemsKnowledge-Based Systems, 2010
- Applications of wavelet data reduction in a recommender systemExpert Systems with Applications, 2008
- Projected Gradient Methods for Nonnegative Matrix FactorizationNeural Computation, 2007
- Trust-inspiring explanation interfaces for recommender systemsKnowledge-Based Systems, 2007
- Collaborative Recommending using Formal Concept AnalysisKnowledge-Based Systems, 2006
- CinemaScreen Recommender Agent: Combining Collaborative and Content-Based FilteringIEEE Intelligent Systems, 2006
- Evaluating collaborative filtering recommender systemsACM Transactions on Information Systems, 2004
- Recommender systemsCommunications of the ACM, 1997
- Positive matrix factorization: A non‐negative factor model with optimal utilization of error estimates of data valuesEnvironmetrics, 1994