SOTAVerified

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 2130 of 554 papers

TitleStatusHype
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential RecommendationCode1
GLoSS: Generative Language Models with Semantic Search for Sequential RecommendationCode1
Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective ApproachCode1
Flow Matching based Sequential Recommender ModelCode1
DIFF: Dual Side-Information Filtering and Fusion for Sequential RecommendationCode1
AlphaFuse: Learn ID Embeddings for Sequential Recommendation in Null Space of Language EmbeddingsCode1
Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair SelectionCode1
Intent-aware Diffusion with Contrastive Learning for Sequential RecommendationCode1
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential RecommendationCode1
Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?Code1
Show:102550
← PrevPage 3 of 56Next →

No leaderboard results yet.