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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 251–275 of 554 papers

TitleStatusHype
LLaRA: Large Language-Recommendation AssistantCode1
E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential RecommendationCode1
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training—0
Scaling Law of Large Sequential Recommendation Models—0
Collaborative Word-based Pre-trained Item Representation for Transferable RecommendationCode1
Mixed Attention Network for Cross-domain Sequential RecommendationCode1
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation—0
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising ApproachCode0
Rethinking Cross-Domain Sequential Recommendation under Open-World AssumptionsCode1
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Learning Robust Sequential Recommenders through Confident Soft LabelsCode0
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential RecommendationCode0
AutoSAM: Towards Automatic Sampling of User Behaviors for Sequential Recommender SystemsCode0
Generate What You Prefer: Reshaping Sequential Recommendation via Guided DiffusionCode1
Large Language Model Can Interpret Latent Space of Sequential RecommenderCode1
Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model—0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems—0
Intent Contrastive Learning with Cross Subsequences for Sequential RecommendationCode1
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation—0
To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential RecommendersCode1
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language—0
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation—0
Farzi Data: Autoregressive Data Distillation—0
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