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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 1–25 of 554 papers

TitleStatusHype
Similarity-Guided Diffusion for Contrastive Sequential Recommendation—0
Unconditional Diffusion for Generative Sequential RecommendationCode0
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential RecommendationCode1
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation—0
A Systematic Replicability and Comparative Study of BSARec and SASRec for Sequential Recommendation—0
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential RecommendationCode2
C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized RecommendationCode0
RecGPT: A Foundation Model for Sequential RecommendationCode2
GLoSS: Generative Language Models with Semantic Search for Sequential RecommendationCode1
Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective ApproachCode1
Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain RecommendationCode0
Flow Matching based Sequential Recommender ModelCode1
LARES: Latent Reasoning for Sequential Recommendation—0
DIFF: Dual Side-Information Filtering and Fusion for Sequential RecommendationCode1
HMamba: Hyperbolic Mamba for Sequential Recommendation—0
M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation—0
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation—0
A Framework for Elastic Adaptation of User Multiple Intents in Sequential Recommendation—0
X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation—0
Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair SelectionCode1
AlphaFuse: Learn ID Embeddings for Sequential Recommendation in Null Space of Language EmbeddingsCode1
Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation—0
Intent-aware Diffusion with Contrastive Learning for Sequential RecommendationCode1
Hierarchical Attention Fusion of Visual and Textual Representations for Cross-Domain Sequential Recommendation—0
Adaptive Long-term Embedding with Denoising and Augmentation for RecommendationCode0
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