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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

Showing 4150 of 554 papers

TitleStatusHype
Preference Diffusion for RecommendationCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Multi-Behavioral Sequential RecommendationCode1
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item CatalogsCode1
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal RecommendationCode1
TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality ModelCode1
Generative Recommender with End-to-End Learnable Item TokenizationCode1
SSD4Rec: A Structured State Space Duality Model for Efficient Sequential RecommendationCode1
MARS: Matching Attribute-aware Representations for Text-based Sequential RecommendationCode1
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session RecommendationCode1
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