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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
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems—0
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation—0
A Systematic Replicability and Comparative Study of BSARec and SASRec for Sequential Recommendation—0
Filtering with Time-frequency Analysis: An Adaptive and Lightweight Model for Sequential Recommender Systems Based on Discrete Wavelet Transform—0
Few-shot Model Extraction Attacks against Sequential Recommender Systems—0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation—0
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai—0
Farzi Data: Autoregressive Data Distillation—0
HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation—0
Learning to Augment for Casual User Recommendation—0
Context-aware Sequential Recommendation—0
A Survey on Sequential Recommendation—0
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation—0
CSRN: Collaborative Sequential Recommendation Networks for News Retrieval—0
ID-Agnostic User Behavior Pre-training for Sequential Recommendation—0
ID-centric Pre-training for Recommendation—0
Extracting Attentive Social Temporal Excitation for Sequential Recommendation—0
A Survey on Reinforcement Learning for Recommender Systems—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
Conditional Denoising Diffusion for Sequential Recommendation—0
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