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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 271–280 of 554 papers

TitleStatusHype
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation—0
To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential RecommendersCode1
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language—0
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation—0
Farzi Data: Autoregressive Data Distillation—0
Memory efficient location recommendation through proximity-aware representation—0
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential RecommendationCode0
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems—0
Linear Recurrent Units for Sequential RecommendationCode1
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation—0
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