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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 351–375 of 554 papers

TitleStatusHype
Pattern-wise Transparent Sequential Recommendation—0
Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation—0
Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Rethinking Large Language Model Architectures for Sequential Recommendations—0
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning AttacksCode0
Privacy-Preserving Cross-Domain Sequential RecommendationCode0
Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision—0
Privacy-Preserving Sequential Recommendation with Collaborative Confusion—0
Prompt-based Multi-interest Learning Method for Sequential RecommendationCode0
A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot TransferCode0
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential RecommendationCode0
A novel diffusion recommendation algorithm based on multi-scale cnn and residual lstm—0
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation—0
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training—0
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
Scaling Law of Large Sequential Recommendation Models—0
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation—0
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising ApproachCode0
Learning Robust Sequential Recommenders through Confident Soft LabelsCode0
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential RecommendationCode0
AutoSAM: Towards Automatic Sampling of User Behaviors for Sequential Recommender SystemsCode0
Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model—0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems—0
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