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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 41–50 of 554 papers

TitleStatusHype
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions—0
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation—0
LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation—0
Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation—0
Benchmarking LLMs in Recommendation Tasks: A Comparative Evaluation with Conventional Recommenders—0
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation—0
HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation—0
Semantic Gaussian Mixture Variational Autoencoder for Sequential RecommendationCode0
Lightweight yet Efficient: An External Attentive Graph Convolutional Network with Positional Prompts for Sequential RecommendationCode0
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