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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 151–160 of 554 papers

TitleStatusHype
Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation—0
Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential RecommendationCode0
Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity—0
Semantic Codebook Learning for Dynamic Recommendation Models—0
GenRec: Generative Sequential Recommendation with Large Language ModelsCode0
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
MaTrRec: Uniting Mamba and Transformer for Sequential RecommendationCode0
Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
MLSA4Rec: Mamba Combined with Low-Rank Decomposed Self-Attention for Sequential Recommendation—0
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