Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review
Cold Start problems in recommender systems pose various challenges in the adoption and
use of recommender systems, especially for new item uptake and new user engagement …
use of recommender systems, especially for new item uptake and new user engagement …
Systematic Review of Recommendation Systems for Course Selection
S Algarni, F Sheldon - Machine Learning and Knowledge Extraction, 2023 - mdpi.com
Course recommender systems play an increasingly pivotal role in the educational
landscape, driving personalization and informed decision-making for students. However …
landscape, driving personalization and informed decision-making for students. However …
RDERL: Reliable deep ensemble reinforcement learning-based recommender system
Recommender systems (RSs) have been employed for many real-world applications
including search engines, social networks, and information retrieval systems as powerful …
including search engines, social networks, and information retrieval systems as powerful …
A novel hybrid recommender system for the tourism domain
In this paper, we develop a novel hybrid recommender system for the tourism domain, which
combines (a) a Bayesian preferences elicitation component which operates by asking the …
combines (a) a Bayesian preferences elicitation component which operates by asking the …
Differentially private recommender system with variational autoencoders
To provide precise recommendations, traditional recommender systems (RS) collect
personal data, user preference and feedback, which are sensitive to each user if such …
personal data, user preference and feedback, which are sensitive to each user if such …
A hybrid recommender system for health supplement e-commerce based on customer data implicit ratings
P Keikhosrokiani, GM Fye - Multimedia Tools and Applications, 2024 - Springer
The personalized product preference and decision-making recommendation systems are
highly demanded to handle big data and to increase service quality of the e-commerce …
highly demanded to handle big data and to increase service quality of the e-commerce …
Introducing CSP dataset: A dataset optimized for the study of the cold start problem in recommender systems
Recommender systems are tools that help users in the decision-making process of choosing
items that may be relevant for them among a vast amount of other items. One of the main …
items that may be relevant for them among a vast amount of other items. One of the main …
User preference interaction fusion and swap attention graph neural network for recommender system
M Li, W Ma, Z Chu - Neural Networks, 2025 - Elsevier
Recommender systems are widely used in various applications. Knowledge graphs are
increasingly used to improve recommendation performance by extracting valuable …
increasingly used to improve recommendation performance by extracting valuable …
BayesSentiRS: Bayesian sentiment analysis for addressing cold start and sparsity in ranking-based recommender systems
LH Wu - Expert Systems with Applications, 2024 - Elsevier
Recommendation systems are widely used to filter massive information. However, they often
face the challenges of cold start and sparsity problems, limiting their effectiveness. Bayesian …
face the challenges of cold start and sparsity problems, limiting their effectiveness. Bayesian …
ColdGAN: an effective cold-start recommendation system for new users based on generative adversarial networks
CC Chen, PL Lai, CY Chen - Applied Intelligence, 2023 - Springer
Research on the problem of new user cold-start recommendation generally leverages user
side information to suggest items to new users. This approach, however, is impractical due …
side information to suggest items to new users. This approach, however, is impractical due …