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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 251–300 of 554 papers

TitleStatusHype
RecMind: Large Language Model Powered Agent For Recommendation—0
Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity—0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation—0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions—0
Recommender Transformers with Behavior Pathways—0
Semantic Codebook Learning for Dynamic Recommendation Models—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
Cross-Domain Sequential Recommendation via Neural Process—0
CSRN: Collaborative Sequential Recommendation Networks for News Retrieval—0
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language—0
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation—0
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation—0
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation—0
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation—0
An Efficient Attention Mechanism for Sequential Recommendation Tasks: HydraRec—0
An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation—0
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation—0
Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation—0
Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling—0
Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation—0
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation—0
Denoising Self-attentive Sequential Recommendation—0
Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers—0
UPRec: User-Aware Pre-training for Recommender Systems—0
DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation—0
Time Lag Aware Sequential Recommendation—0
Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems—0
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models—0
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models—0
DiPS: Differentiable Policy for Sketching in Recommender Systems—0
Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation—0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks—0
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation—0
Rethinking Large Language Model Architectures for Sequential Recommendations—0
Dual Conditional Diffusion Models for Sequential Recommendation—0
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention—0
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
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