Deep learning and embedding based latent factor model for collaborative recommender systems

A Tegene, Q Liu, Y Gan, T Dai, H Leka, M Ayenew - Applied Sciences, 2023 - mdpi.com
A collaborative recommender system based on a latent factor model has achieved
significant success in the field of personalized recommender systems. However, the latent …

Hybrid structural graph attention network for POI recommendation

J Zhang, W Ma - Expert Systems with Applications, 2024 - Elsevier
In the era of big data, information overload poses a challenge, complicating user decision-
making. Recommender systems aim to assist in this process. In recent years, research on …

A design and implementation of real-time product selection with matrix factorization, collaborative filtering

K Saikumar, ANZ Rashed, SRA Kadeem… - AIP Conference …, 2023 - pubs.aip.org
Product quantified collaboration Filtering and its modification were suggested in this
assignment to learn semiorganized inactive components for items (or clients) based on …

Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation

J Zhang, W Ma, E Zhang, X Xia - Sensors, 2024 - mdpi.com
Technological progress has led to significant advancements in Earth observation and
satellite systems. However, some services associated with remote sensing face issues …

A Hybrid CNN-GRU model for Session-based Recommender Systems

AT Tegene, Q Liu, Y Gan, E Jimale… - … on Computer and …, 2023 - ieeexplore.ieee.org
In many e-commerce recommendation systems, user profiles are mostly not available. In
such cases, the recommendation task considers user behaviors like click sessions or …

[PDF][PDF] Product Recommendation System

B Sakaram, HSR Pasam, S Mullamuri - researchgate.net
Product recommendation systems have seen significant advancements due to various
algorithms and approaches aimed at improving user experience and engagement …