Artificial intelligence in E-Commerce: a bibliometric study and literature review
This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes
guidelines on how information systems (IS) research could contribute to this research …
guidelines on how information systems (IS) research could contribute to this research …
Evaluating collaborative filtering recommender algorithms: a survey
Due to the explosion of available information on the Internet, the need for effective means of
accessing and processing them has become vital for everyone. Recommender systems …
accessing and processing them has become vital for everyone. Recommender systems …
An α–β-divergence-generalized recommender for highly accurate predictions of missing user preferences
To quantify user–item preferences, a recommender system (RS) commonly adopts a high-
dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative …
dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative …
A fusion collaborative filtering method for sparse data in recommender systems
C Feng, J Liang, P Song, Z Wang - Information Sciences, 2020 - Elsevier
Collaborative filtering is a fundamental technique in recommender systems, for which
memory-based and matrix-factorization-based collaborative filtering are the two types of …
memory-based and matrix-factorization-based collaborative filtering are the two types of …
A multilayered-and-randomized latent factor model for high-dimensional and sparse matrices
How to extract useful knowledge from a high-dimensional and sparse (HiDS) matrix
efficiently is critical for many big data-related applications. A latent factor (LF) model has …
efficiently is critical for many big data-related applications. A latent factor (LF) model has …
[HTML][HTML] A novel model based collaborative filtering recommender system via truncated ULV decomposition
F Horasan, AH Yurttakal, S Gündüz - … of King Saud University-Computer and …, 2023 - Elsevier
Collaborative filtering is a technique that takes into account the common characteristics of
users and items in recommender systems. Matrix decompositions are one of the most used …
users and items in recommender systems. Matrix decompositions are one of the most used …
Pair-wise preference relation based probabilistic matrix factorization for collaborative filtering in recommender system
A Pujahari, DS Sisodia - Knowledge-Based Systems, 2020 - Elsevier
Matrix Factorization (MF) is one of the most popular techniques used in Collaborative
Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …
Filtering (CF) based Recommender System (RS). Most of the MF methods tend to remove …
Adaptively-accelerated Parallel Stochastic Gradient Descent for High-Dimensional and Incomplete Data Representation Learning
High-dimensional and incomplete (HDI) interactions among numerous nodes are commonly
encountered in a Big Data-related application, like user-item interactions in a recommender …
encountered in a Big Data-related application, like user-item interactions in a recommender …
Generalized fractional strategy for recommender systems with chaotic ratings behavior
To provide useful and accurate recommendations, the role of recommender systems for e-
commerce industry is to predict users' interest by approximating users' preferences and …
commerce industry is to predict users' interest by approximating users' preferences and …
A comparative analysis of bias amplification in graph neural network approaches for recommender systems
Recommender Systems (RSs) are used to provide users with personalized item
recommendations and help them overcome the problem of information overload. Currently …
recommendations and help them overcome the problem of information overload. Currently …