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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 2130 of 554 papers

TitleStatusHype
Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-AttentionCode1
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment RecommendationCode1
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential RecommendationCode1
A Self-Correcting Sequential RecommenderCode1
A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-SharingCode1
Black-Box Attacks on Sequential Recommenders via Data-Free Model ExtractionCode1
AlphaFuse: Learn ID Embeddings for Sequential Recommendation in Null Space of Language EmbeddingsCode1
A Generic Network Compression Framework for Sequential Recommender SystemsCode1
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