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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 226–250 of 554 papers

TitleStatusHype
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential RecommendationCode1
BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation—0
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
Improving Sequential Recommendations with LLMsCode2
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
End-to-end Learnable Clustering for Intent Learning in RecommendationCode2
A Survey on Cross-Domain Sequential RecommendationCode1
Privacy-Preserving Sequential Recommendation with Collaborative Confusion—0
Prompt-based Multi-interest Learning Method for Sequential RecommendationCode0
Plug-in Diffusion Model for Sequential RecommendationCode1
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
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k RecommendationCode1
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation—0
A novel diffusion recommendation algorithm based on multi-scale cnn and residual lstm—0
An Attentive Inductive Bias for Sequential Recommendation beyond the Self-AttentionCode1
Context-Aware Sequential Model for Multi-Behaviour RecommendationCode1
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System ExposureCode1
RecJPQ: Training Large-Catalogue Sequential RecommendersCode1
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