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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 351–400 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
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation—0
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language—0
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation—0
Farzi Data: Autoregressive Data Distillation—0
Memory efficient location recommendation through proximity-aware representation—0
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential RecommendationCode0
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems—0
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation—0
Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation—0
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation—0
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language ModelingCode0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation—0
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential RecommendationCode0
Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach—0
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task LearningCode0
A Survey on Multi-Behavior Sequential Recommendation—0
RecMind: Large Language Model Powered Agent For Recommendation—0
Invariant representation learning for sequential recommendation—0
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation—0
AutoSeqRec: Autoencoder for Efficient Sequential RecommendationCode0
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential RecommendationCode0
Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation—0
Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation—0
Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation—0
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning—0
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