Recommender systems based on graph embedding techniques: A review
Y Deng - IEEE Access, 2022 - ieeexplore.ieee.org
As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …
predict user's preferred items from millions of candidates by analyzing observed user-item …
LightFR: Lightweight federated recommendation with privacy-preserving matrix factorization
Federated recommender system (FRS), which enables many local devices to train a shared
model jointly without transmitting local raw data, has become a prevalent recommendation …
model jointly without transmitting local raw data, has become a prevalent recommendation …
Socially-aware dual contrastive learning for cold-start recommendation
Social recommendation with Graph Neural Networks (GNNs) learns to represent cold users
by fusing user-user social relations with user-item interactions, thereby alleviating the cold …
by fusing user-user social relations with user-item interactions, thereby alleviating the cold …
[HTML][HTML] Reinforcement learning based trustworthy recommendation model for digital twin-driven decision-support in manufacturing systems
Digital twin is one promising and key technology that emerged with Industry 4.0 to assist the
decision-making process in multiple industries, enabling potential benefits such as reducing …
decision-making process in multiple industries, enabling potential benefits such as reducing …
A survey for trust-aware recommender systems: A deep learning perspective
A significant remaining challenge for existing recommender systems is that users may not
trust recommender systems for either inaccurate recommendation or lack of explanation …
trust recommender systems for either inaccurate recommendation or lack of explanation …
PrivFR: Privacy-Enhanced Federated Recommendation With Shared Hash Embedding
Federated recommender systems (FRSs), with their improved privacy-preserving
advantages to jointly train recommendation models from numerous devices while kee** …
advantages to jointly train recommendation models from numerous devices while kee** …
Grecx: An efficient and unified benchmark for GNN-based recommendation
In this paper, we present GRecX, an open-source TensorFlow framework for benchmarking
GNN-based recommendation models in an efficient and unified way. GRecX consists of core …
GNN-based recommendation models in an efficient and unified way. GRecX consists of core …
Neural binary representation learning for large-scale collaborative filtering
Y Zhang, J Wu, H Wang - IEEE Access, 2019 - ieeexplore.ieee.org
Integrating hashing into collaborative filtering (CF) has been a promising solution to address
the efficiency problem of large-scale recommender systems, ie, hashing users and items into …
the efficiency problem of large-scale recommender systems, ie, hashing users and items into …
Integrating dual user network embedding with matrix factorization for social recommender systems
To address the data sparsity problem faced by recommender systems, social network
among users is often utilized to complement rating data for improving the recommendation …
among users is often utilized to complement rating data for improving the recommendation …
Deep Collaborative Filtering Recommendation Algorithm Based on Sentiment Analysis
D Ao, C Zhang - 2023 8th International Conference on …, 2023 - ieeexplore.ieee.org
Data sparsity is the main challenge that recommendation algorithms have been facing. E-
commerce recommendation mainly focus on the explicit interaction between users and items …
commerce recommendation mainly focus on the explicit interaction between users and items …