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Found 9 papers, 4 papers with code
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation
To this end, we propose an External Attention-enhanced Graph Contrastive Learning framework, namely EA-GCL.
Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation
Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information.
Output tracking based on extended observer for nonlinear uncertain systems
A high-gain extended observer is designed for a class of nonlinear uncertain systems.
Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network
Cross-domain Sequential Recommendation (CSR) is an emerging yet challenging task that depicts the evolution of behavior patterns for overlapped users by modeling their interactions from multiple domains.
Signal corrector and decoupling estimations for UAV control
For a class of uncertain systems with large-error sensing, the low-order stable signal corrector and observer are presented for signal correction and uncertainty estimation according to completely decoupling estimation.
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Tri-Branch Convolutional Neural Networks for Top-$k$ Focused Academic Performance Prediction
Academic performance prediction aims to leverage student-related information to predict their future academic outcomes, which is beneficial to numerous educational applications, such as personalized teaching and academic early warning.
Task-adaptive Asymmetric Deep Cross-modal Hashing
Unlike previous cross-modal hashing approaches, our learning framework jointly optimizes semantic preserving that transforms deep features of multimedia data into binary hash codes, and the semantic regression which directly regresses query modality representation to explicit label.
Topic representation: finding more representative words in topic models
The top word list, i. e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models.
