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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 291–300 of 554 papers

TitleStatusHype
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation—0
Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models—0
Direct Preference Optimization for LLM-Enhanced Recommendation Systems—0
Route Optimization via Environment-Aware Deep Network and Reinforcement Learning—0
Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation—0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation—0
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation—0
Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items—0
ELASTIC: Efficient Linear Attention for Sequential Interest Compression—0
Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation—0
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