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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

Showing 391400 of 554 papers

TitleStatusHype
Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions0
Recommender Transformers with Behavior Pathways0
Semantic Codebook Learning for Dynamic Recommendation Models0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation0
Cross-Domain Sequential Recommendation via Neural Process0
CSRN: Collaborative Sequential Recommendation Networks for News Retrieval0
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language0
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation0
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