Survey of similarity functions on neighborhood-based collaborative filtering

H Khojamli, J Razmara - Expert Systems with Applications, 2021 - Elsevier
Today, recommender systems play a vital role in the acceleration of searches by internet
users to find what they are interested in. Among the strategies proposed for recommender …

Collaborative filtering with temporal features for movie recommendation system

G Behera, N Nain - Procedia Computer Science, 2023 - Elsevier
Nowadays, recommender systems play a vital role in every human being's life due to the
time retrieving the items. The matrix factorization (MF) technique is one of the main methods …

The explainable structure of deep neural network for recommendation systems

MD Zanjani, MH Aghdam - Future Generation Computer Systems, 2024 - Elsevier
Recommender systems (RS) play a pivotal role in establishing user trust by suggesting
relevant items that meet their needs and enhance reliability. The recent trend involves …

PEVRM: probabilistic evolution based version recommendation model for mobile applications

M Maheswari, S Geetha, SS Kumar, M Karuppiah… - IEEE …, 2021 - ieeexplore.ieee.org
Traditional recommendation approaches for the mobile Apps basically depend on the Apps
related features. Now a days many users are in quench of Apps recommendation based on …

A novel regularized asymmetric non-negative matrix factorization for text clustering

MH Aghdam, MD Zanjani - Information Processing & Management, 2021 - Elsevier
Non-negative matrix factorization (NMF) is a dimension reduction method that extracts
semantic features from high-dimensional data. Most of the developed optimization methods …

Context-aware recommender systems using hierarchical hidden Markov model

MH Aghdam - Physica A: Statistical Mechanics and Its Applications, 2019 - Elsevier
Recommender systems often generate recommendations based on user's prior preferences.
Users' preferences may change over time due to user mode change or context change …

DCARS: Deep context-aware recommendation system based on session latent context

J Sohafi-Bonab, MH Aghdam, K Majidzadeh - Applied Soft Computing, 2023 - Elsevier
Recommendation systems (RSs) usually create suggestions based on users' prior
intentions. Users' interests may evolve due to context change or user-mode change …

A novel constrained non-negative matrix factorization method based on users and items pairwise relationship for recommender systems

MH Aghdam - Expert Systems with Applications, 2022 - Elsevier
Non-negative matrix factorization (NMF) is a famous method to learn parts-based
representations of non-negative data. It has been used successfully in various applications …

KT-CDULF: Knowledge Transfer in Context-Aware Cross-Domain Recommender Systems via Latent User Profiling

AA Cheema, MS Sarfraz, M Usman, QU Zaman… - IEEE …, 2024 - ieeexplore.ieee.org
Recommender systems are crucial in today's digital world, by enhancing user engagement
experience in digital ecosystems. Internet of things (IoT) have huge potential to generate …

Matrix factorization in recommender systems: algorithms, applications, and peculiar challenges

FO Isinkaye - IETE Journal of Research, 2023 - Taylor & Francis
Traditional Collaborative filtering (CF) is one of the techniques of recommender systems that
has been successfully exploited in various applications, but sometimes they fail to provide …