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 501510 of 554 papers

TitleStatusHype
Improving Sequential Recommendation Models with an Enhanced Loss FunctionCode0
Empowering Sequential Recommendation from Collaborative Signals and Semantic RelatednessCode0
TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential RecommendationCode0
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionCode0
Improving End-to-End Sequential Recommendations with Intent-aware DiversificationCode0
Attacking Pre-trained RecommendationCode0
Data Watermarking for Sequential Recommender SystemsCode0
Improving Minimax Group Fairness in Sequential RecommendationCode0
XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender SystemCode0
SSE-PT: Sequential Recommendation Via Personalized TransformerCode0
Show:102550
← PrevPage 51 of 56Next →

No leaderboard results yet.