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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 351–360 of 554 papers

TitleStatusHype
A Systematic Replicability and Comparative Study of BSARec and SASRec for Sequential Recommendation—0
Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation—0
Attention-based sequential recommendation system using multimodal data—0
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation—0
Privacy-Preserving Sequential Recommendation with Collaborative Confusion—0
Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation—0
Autoregressive Generation Strategies for Top-K Sequential Recommendations—0
A Zero-Shot Generalization Framework for LLM-Driven Cross-Domain Sequential Recommendation—0
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation—0
Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders—0
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