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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 311320 of 554 papers

TitleStatusHype
HTP: Exploiting Holistic Temporal Patterns for Sequential RecommendationCode0
AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation0
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation0
Reciprocal Sequential RecommendationCode1
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender SystemsCode2
Generative Sequential Recommendation with GPTRec0
Neighborhood-based Hard Negative Mining for Sequential RecommendationCode0
Efficient Failure Pattern Identification of Predictive AlgorithmsCode1
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