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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 4150 of 554 papers

TitleStatusHype
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
Contrastive Learning for Representation Degeneration Problem in Sequential RecommendationCode1
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
A Self-Correcting Sequential RecommenderCode1
A Survey on Cross-Domain Sequential RecommendationCode1
A Contextual-Aware Position Encoding for Sequential RecommendationCode1
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationCode1
A Generic Network Compression Framework for Sequential Recommender SystemsCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
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