Strategy-aware bundle recommender system
A bundle is a group of items that provides improved services to users and increased profits
for sellers. However, locating the desired bundles that match the users' tastes still …
for sellers. However, locating the desired bundles that match the users' tastes still …
Coarse-to-fine knowledge-enhanced multi-interest learning framework for multi-behavior recommendation
Multi-types of behaviors (eg, clicking, carting, purchasing, etc.) widely exist in most real-
world recommendation scenarios, which are beneficial to learn users' multi-faceted …
world recommendation scenarios, which are beneficial to learn users' multi-faceted …
Learning from hierarchical structure of knowledge graph for recommendation
Knowledge graphs (KGs) can help enhance recommendations, especially for the data-
sparsity scenarios with limited user-item interaction data. Due to the strong power of …
sparsity scenarios with limited user-item interaction data. Due to the strong power of …
Semantic-Guided Feature Distillation for Multimodal Recommendation
Multimodal recommendation exploits the rich multimodal information associated with users
or items to enhance the representation learning for better performance. In these methods …
or items to enhance the representation learning for better performance. In these methods …
Parallel knowledge enhancement based framework for multi-behavior recommendation
Multi-behavior recommendation algorithms aim to leverage the multiplex interactions
between users and items to learn users' latent preferences. Recent multi-behavior …
between users and items to learn users' latent preferences. Recent multi-behavior …
A novel joint neural collaborative filtering incorporating rating reliability
J Deng, Q Wu, S Wang, J Ye, P Wang, M Du - Information Sciences, 2024 - Elsevier
Deep learning-based recommendations have demonstrated impressive performance in
improving recommendation accuracy. However, such approaches mainly utilize implicit …
improving recommendation accuracy. However, such approaches mainly utilize implicit …
Contrastive state augmentations for reinforcement learning-based recommender systems
Learning reinforcement learning (RL)-based recommenders from historical user-item
interaction sequences is vital to generate high-reward recommendations and improve long …
interaction sequences is vital to generate high-reward recommendations and improve long …
SMEF: Social-aware Multi-dimensional Edge Features-based Graph Representation Learning for Recommendation
Exploring user-item interaction cues is crucial for the performance of recommender systems.
Explicit investigation of interaction cues is made possible by using graph-based models …
Explicit investigation of interaction cues is made possible by using graph-based models …
Improving implicit feedback-based recommendation through multi-behavior alignment
Recommender systems that learn from implicit feedback often use large volumes of a single
type of implicit user feedback, such as clicks, to enhance the prediction of sparse target …
type of implicit user feedback, such as clicks, to enhance the prediction of sparse target …
Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models
Recommendation systems harness user-item interactions like clicks and reviews to learn
their representations. Previous studies improve recommendation accuracy and …
their representations. Previous studies improve recommendation accuracy and …