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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 191–200 of 554 papers

TitleStatusHype
Improve Temporal Awareness of LLMs for Sequential Recommendation—0
CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation—0
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?—0
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement—0
TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal ContentCode0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation—0
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential RecommendationCode0
FineRec:Exploring Fine-grained Sequential RecommendationCode1
The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential RecommendationCode1
Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model—0
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