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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 376–400 of 554 papers

TitleStatusHype
CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation—0
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning—0
CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation—0
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning—0
Coarse-to-Fine Sparse Sequential Recommendation—0
Cold-start Sequential Recommendation via Meta Learner—0
RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm—0
Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs—0
Conditional Denoising Diffusion for Sequential Recommendation—0
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction—0
Context-aware Sequential Recommendation—0
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai—0
Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation—0
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement—0
RecMind: Large Language Model Powered Agent For Recommendation—0
Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity—0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation—0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions—0
Recommender Transformers with Behavior Pathways—0
Semantic Codebook Learning for Dynamic Recommendation Models—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
Cross-Domain Sequential Recommendation via Neural Process—0
CSRN: Collaborative Sequential Recommendation Networks for News Retrieval—0
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language—0
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation—0
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