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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 76–100 of 554 papers

TitleStatusHype
Diffusion-based Contrastive Learning for Sequential RecommendationCode1
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential RecommendationCode1
Dually Enhanced Propensity Score Estimation in Sequential RecommendationCode1
E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential RecommendationCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Efficient Failure Pattern Identification of Predictive AlgorithmsCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
Contrastive Learning for Sequential RecommendationCode1
Generative Recommender with End-to-End Learnable Item TokenizationCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Explanation Guided Contrastive Learning for Sequential RecommendationCode1
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-AttentionCode1
Advances in Collaborative Filtering and RankingCode1
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential RecommendationCode1
FineRec:Exploring Fine-grained Sequential RecommendationCode1
A Self-Correcting Sequential RecommenderCode1
Adversarial and Contrastive Variational Autoencoder for Sequential RecommendationCode1
A Survey on Cross-Domain Sequential RecommendationCode1
Generate What You Prefer: Reshaping Sequential Recommendation via Guided DiffusionCode1
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
Collaborative Word-based Pre-trained Item Representation for Transferable RecommendationCode1
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationCode1
Controllable Multi-Interest Framework for RecommendationCode1
An Empirical Study of Training ID-Agnostic Multi-modal Sequential RecommendersCode1
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System ExposureCode1
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