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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 421430 of 554 papers

TitleStatusHype
Modeling Dynamic User Preference via Dictionary Learning for Sequential RecommendationCode0
Learning to Augment for Casual User Recommendation0
Sequential Recommendation with Causal Behavior Discovery0
Sequential Recommendation with User Evolving Preference Decomposition0
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential RecommendationCode1
Improving Contrastive Learning with Model AugmentationCode1
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)Code2
ARERec: Attentive Local Interaction Model for Sequential RecommendationCode0
Symmetry Structured Convolutional Neural NetworksCode0
PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application0
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