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Sequential Recommendation

Sequential recommendation is a sophisticated approach to providing personalized suggestions by analyzing users' historical interactions in a sequential manner. Unlike traditional recommendation systems, which consider items in isolation, sequential recommendation takes into account the temporal order of user actions. This method is particularly valuable in domains where the sequence of events matters, such as streaming services, e-commerce platforms, and social media.

Papers

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Showing 451–500 of 554 papers

TitleStatusHype
Generating Negative Samples for Sequential Recommendation—0
Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation—0
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential RecommendationCode0
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential RecommendationCode0
Recommender Transformers with Behavior Pathways—0
ID-Agnostic User Behavior Pre-training for Sequential Recommendation—0
Enhancing Sequential Recommendation with Graph Contrastive Learning—0
Personalized Prompt for Sequential Recommendation—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation—0
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings—0
Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers—0
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
Coarse-to-Fine Sparse Sequential Recommendation—0
Learning to Augment for Casual User Recommendation—0
Modeling Dynamic User Preference via Dictionary Learning for Sequential RecommendationCode0
Sequential Recommendation with Causal Behavior Discovery—0
Sequential Recommendation with User Evolving Preference Decomposition—0
ARERec: Attentive Local Interaction Model for Sequential RecommendationCode0
Symmetry Structured Convolutional Neural NetworksCode0
PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application—0
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models—0
NxtPost: User to Post Recommendations in Facebook Groups—0
Sequential Search with Off-Policy Reinforcement Learning—0
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and ItemsCode0
CSSR: A Context-Aware Sequential Software Service Recommendation ModelCode0
GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation—0
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-trainingCode0
DiPS: Differentiable Policy for Sketching in Recommender Systems—0
Gumble Softmax For User Behavior Modeling—0
Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation—0
Route Optimization via Environment-Aware Deep Network and Reinforcement Learning—0
TEA: A Sequential Recommendation Framework via Temporally Evolving AggregationsCode0
Supervised Advantage Actor-Critic for Recommender Systems—0
Sequential Movie Genre Prediction using Average Transition Probability with Clustering—0
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning—0
Learning to Learn a Cold-start Sequential Recommender—0
Self-supervised Learning for Sequential Recommendation with Model Augmentation—0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
A Survey on Reinforcement Learning for Recommender Systems—0
CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation—0
Lightweight Self-Attentive Sequential Recommendation—0
Is News Recommendation a Sequential Recommendation Task?—0
Personalized next-best action recommendation with multi-party interaction learning for automated decision-making—0
MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation—0
A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models—0
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
Improving Transformer-based Sequential Recommenders through Preference Editing—0
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