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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 111–120 of 554 papers

TitleStatusHype
Multi-Behavioral Sequential RecommendationCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Direct Preference Optimization for LLM-Enhanced Recommendation Systems—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
Multimodal Point-of-Interest Recommendation—0
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential RecommendationCode2
TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential RecommendationCode0
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item CatalogsCode1
Autoregressive Generation Strategies for Top-K Sequential Recommendations—0
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal RecommendationCode1
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