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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 231240 of 554 papers

TitleStatusHype
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and ItemsCode0
TriMLP: Revenge of a MLP-like Architecture in Sequential RecommendationCode0
HAM: Hybrid Associations Models for Sequential RecommendationCode0
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item PredictionCode0
Improving Sequential Recommendation Models with an Enhanced Loss FunctionCode0
Modeling Dynamic User Preference via Dictionary Learning for Sequential RecommendationCode0
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential RecommendationCode0
Hierarchical Context enabled Recurrent Neural Network for RecommendationCode0
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential RecommendationCode0
ARERec: Attentive Local Interaction Model for Sequential RecommendationCode0
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