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 …
users to find what they are interested in. Among the strategies proposed for recommender …
Collaborative filtering with temporal features for movie recommendation system
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 …
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 …
relevant items that meet their needs and enhance reliability. The recent trend involves …
PEVRM: probabilistic evolution based version recommendation model for mobile applications
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 …
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 …
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 …
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 …
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 …
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
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 …
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 …
has been successfully exploited in various applications, but sometimes they fail to provide …