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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 221–230 of 554 papers

TitleStatusHype
Can Small Language Models be Good Reasoners for Sequential Recommendation?—0
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space ModelsCode2
Sequence-level Semantic Representation Fusion for Recommender SystemsCode1
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation—0
MACRec: a Multi-Agent Collaboration Framework for RecommendationCode2
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential RecommendationCode1
BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation—0
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
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