Enhancing robustness in implicit feedback recommender systems with subgraph contrastive learning
Y Yang, S Guan, X Wen - Information Processing & Management, 2025 - Elsevier
Contrastive learning operates by distinguishing differences between various nodes to
facilitate item recommendations. However, current graph contrastive learning (GCL) …
facilitate item recommendations. However, current graph contrastive learning (GCL) …
Understanding and modeling user behavior for recommendation systems
S Bhogan, VS Rajpurohit, SS Sannakki - AIP Conference Proceedings, 2024 - pubs.aip.org
E-commerce has revolutionized consumer-business interactions through global accessibility
and tailored services. However, existing session-based, sentiment-based, and location …
and tailored services. However, existing session-based, sentiment-based, and location …
Recommendation systems: enhancing personalization and customer experience
S Silvester, S Kurian - 2023 3rd International Conference on …, 2023 - ieeexplore.ieee.org
In the ever-evolving landscape of e-commerce, the integration of recommendation systems
has emerged as a pivotal system for refining personalization strategies and elevating …
has emerged as a pivotal system for refining personalization strategies and elevating …
False Negative Sample Aware Negative Sampling for Recommendation
Negative sampling plays a key role in implicit feedback collaborative filtering. It draws high-
quality negative samples from a large number of uninteracted samples. Existing methods …
quality negative samples from a large number of uninteracted samples. Existing methods …