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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 241250 of 554 papers

TitleStatusHype
Plug-in Diffusion Model for Sequential RecommendationCode1
A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot TransferCode0
Curriculum-scheduled Knowledge Distillation from Multiple Pre-trained Teachers for Multi-domain Sequential RecommendationCode0
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k RecommendationCode1
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation0
A novel diffusion recommendation algorithm based on multi-scale cnn and residual lstm0
An Attentive Inductive Bias for Sequential Recommendation beyond the Self-AttentionCode1
Context-Aware Sequential Model for Multi-Behaviour RecommendationCode1
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System ExposureCode1
RecJPQ: Training Large-Catalogue Sequential RecommendersCode1
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