Sequential Recommendation
191 papers with code • 8 benchmarks • 8 datasets
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.
Benchmarks
These leaderboards are used to track progress in Sequential Recommendation
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Libraries
Use these libraries to find Sequential Recommendation models and implementations
MovieLens
Yelp
X-Wines
Most implemented papers
SSE-PT: Sequential Recommendation Via Personalized Transformer
SSE-PT/SSE-PT
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Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed for better use of the temporal ordering of items that each user has engaged with.
HAM: Hybrid Associations Models for Sequential Recommendation
BoPeng112/HAM
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We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings.
Controllable Multi-Interest Framework for Recommendation
THUDM/ComiRec
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Recent works usually give an overall embedding from a user's behavior sequence.
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
ReyonRen/MFGAN
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Recently, deep learning has made significant progress in the task of sequential recommendation.
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture.
A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation.
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
In this paper, we propose a novel sequential recommendation framework to overcome these challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec.
Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
In this paper, both empirical and theoretical investigations of this representation degeneration problem are first provided, based on which a novel recommender model DuoRec is proposed to improve the item embeddings distribution.
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations.
Filter-enhanced MLP is All You Need for Sequential Recommendation
Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation.
