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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 171–180 of 554 papers

TitleStatusHype
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems—0
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential RecommendationCode0
Your Causal Self-Attentive Recommender Hosts a Lonely NeighborhoodCode0
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential RecommendationCode0
Towards commands recommender system in BIM authoring tool using transformers—0
A Practice-Friendly LLM-Enhanced Paradigm with Preference Parsing for Sequential Recommendation—0
Information Maximization via Variational Autoencoders for Cross-Domain Recommendation—0
SelfGNN: Self-Supervised Graph Neural Networks for Sequential RecommendationCode2
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential RecommendationCode2
Attention-based sequential recommendation system using multimodal data—0
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