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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 211–220 of 554 papers

TitleStatusHype
A Novel Mamba-based Sequential Recommendation Method—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation—0
AdaptRec: A Self-Adaptive Framework for Sequential Recommendations with Large Language Models—0
Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation—0
Filtering with Time-frequency Analysis: An Adaptive and Lightweight Model for Sequential Recommender Systems Based on Discrete Wavelet Transform—0
Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation—0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions—0
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation—0
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