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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 451460 of 554 papers

TitleStatusHype
CosRec: 2D Convolutional Neural Networks for Sequential RecommendationCode0
Personalized Top-N Sequential Recommendation via Convolutional Sequence EmbeddingCode0
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential RecommendationCode0
Robust Reinforcement Learning Objectives for Sequential Recommender SystemsCode0
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential RecommendationCode0
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task LearningCode0
Positional encoding is not the same as context: A study on positional encoding for sequential recommendationCode0
TEA: A Sequential Recommendation Framework via Temporally Evolving AggregationsCode0
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential RecommendationCode0
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
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