Large-scale and scalable latent factor analysis via distributed alternative stochastic gradient descent for recommender systems
Latent factor analysis (LFA) via stochastic gradient descent (SGD) is highly efficient in
discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from …
discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from …
Parallel and distributed collaborative filtering: A survey
Collaborative filtering is among the most preferred techniques when implementing
recommender systems. Recently, great interest has turned toward parallel and distributed …
recommender systems. Recently, great interest has turned toward parallel and distributed …
A parallel matrix factorization based recommender by alternating stochastic gradient decent
X Luo, H Liu, G Gou, Y **a, Q Zhu - Engineering Applications of Artificial …, 2012 - Elsevier
Collaborative Filtering (CF) can be achieved by Matrix Factorization (MF) with high
prediction accuracy and scalability. Most of the current MF based recommenders, however …
prediction accuracy and scalability. Most of the current MF based recommenders, however …
Construction of a triglyceride amperometric biosensor based on chitosan–ZnO nanocomposite film
J Narang, CS Pundir - International journal of biological macromolecules, 2011 - Elsevier
A method is described for construction of a novel amperometric triglyceride (TG) biosensor
based on covalent co-immobilization of lipase, glycerol kinase (GK) and glycerol-3 …
based on covalent co-immobilization of lipase, glycerol kinase (GK) and glycerol-3 …
A scalable clustering algorithm for serendipity in recommender systems
High sparsity and the problem of overspecialization are challenges faced by collaborative
filtering (CF) algorithms in recommender systems. In this paper, we design an approach that …
filtering (CF) algorithms in recommender systems. In this paper, we design an approach that …
Parametric evaluation of collaborative filtering over apache spark
A Alexopoulos, G Drakopoulos… - 2020 5th South-East …, 2020 - ieeexplore.ieee.org
Recommender systems are mechanisms that filter information in order to predict the
preference of a user for an item drawn from a finite collection. Prime examples include …
preference of a user for an item drawn from a finite collection. Prime examples include …
High performance offline and online distributed collaborative filtering
Big data analytics is a hot research area both in academia and industry. It envisages
processing massive amounts of data at high rates to generate new insights leading to …
processing massive amounts of data at high rates to generate new insights leading to …
Parallel implementation of the slope one algorithm for collaborative filtering
Recommender systems are mechanisms that filter information and predict a user's
preference to an item. Parallel implementations of recommender systems improve scalability …
preference to an item. Parallel implementations of recommender systems improve scalability …
Lcbm: a fast and lightweight collaborative filtering algorithm for binary ratings
In the last ten years, recommendation systems evolved from novelties to powerful business
tools, deeply changing the internet industry. Collaborative Filtering (CF) represents a widely …
tools, deeply changing the internet industry. Collaborative Filtering (CF) represents a widely …
LCBM: Statistics-based parallel collaborative filtering
In the last ten years, recommendation systems evolved from novelties to powerful business
tools, deeply changing the internet industry. Collaborative Filtering (CF) represents today'sa …
tools, deeply changing the internet industry. Collaborative Filtering (CF) represents today'sa …