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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 361370 of 554 papers

TitleStatusHype
Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation0
Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model0
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs0
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation0
BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation0
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model0
Break the ID-Language Barrier: An Adaption Framework for Sequential Recommendation0
Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation0
Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation0
Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models0
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