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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 61–70 of 554 papers

TitleStatusHype
Multi-Grained Patch Training for Efficient LLM-based RecommendationCode0
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential RecommendationCode0
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential RecommendationCode1
Style4Rec: Enhancing Transformer-based E-commerce Recommendation Systems with Style and Shopping Cart Information—0
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation—0
An Efficient Attention Mechanism for Sequential Recommendation Tasks: HydraRec—0
Image Fusion for Cross-Domain Sequential Recommendation—0
RaSeRec: Retrieval-Augmented Sequential RecommendationCode1
Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation—0
Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models—0
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