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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 491500 of 554 papers

TitleStatusHype
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item PredictionCode0
Hierarchical Gating Networks for Sequential RecommendationCode0
Preference Distillation for Personalized Generative RecommendationCode0
A Hierarchical Contextual Attention-based GRU Network for Sequential RecommendationCode0
Modeling Dynamic User Preference via Dictionary Learning for Sequential RecommendationCode0
HTP: Exploiting Holistic Temporal Patterns for Sequential RecommendationCode0
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential RecommendationCode0
TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal ContentCode0
Dual-interest Factorization-heads Attention for Sequential RecommendationCode0
Modeling Sequential Recommendation as Missing Information ImputationCode0
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