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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 321–330 of 554 papers

TitleStatusHype
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems—0
Style4Rec: Enhancing Transformer-based E-commerce Recommendation Systems with Style and Shopping Cart Information—0
On Modeling Long-Term User Engagement from Stochastic Feedback—0
A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models—0
Superposition-Assisted Stochastic Optimization for Hawkes Processes—0
Supervised Advantage Actor-Critic for Recommender Systems—0
PALR: Personalization Aware LLMs for Recommendation—0
Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations—0
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation—0
Pattern-wise Transparent Sequential Recommendation—0
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