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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 341–350 of 554 papers

TitleStatusHype
Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy—0
A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation—0
Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation—0
A Review-Driven Neural Model for Sequential Recommendation—0
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?—0
ULMRec: User-centric Large Language Model for Sequential Recommendation—0
Preference Discerning with LLM-Enhanced Generative Retrieval—0
A Survey on Multi-Behavior Sequential Recommendation—0
A Survey on Reinforcement Learning for Recommender Systems—0
A Survey on Sequential Recommendation—0
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