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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 401–410 of 554 papers

TitleStatusHype
HTP: Exploiting Holistic Temporal Patterns for Sequential RecommendationCode0
AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation—0
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation—0
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer—0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
Generative Sequential Recommendation with GPTRec—0
Neighborhood-based Hard Negative Mining for Sequential RecommendationCode0
Robust Reinforcement Learning Objectives for Sequential Recommender SystemsCode0
TriMLP: Revenge of a MLP-like Architecture in Sequential RecommendationCode0
PALR: Personalization Aware LLMs for Recommendation—0
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