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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
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
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