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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
Your Causal Self-Attentive Recommender Hosts a Lonely NeighborhoodCode0
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
Attention-based sequential recommendation system using multimodal data—0
Look into the Future: Deep Contextualized Sequential Recommendation—0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
Positional encoding is not the same as context: A study on positional encoding for sequential recommendationCode0
ID-centric Pre-training for Recommendation—0
Improve Temporal Awareness of LLMs for Sequential Recommendation—0
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