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MovieLensL-1m
MovieLensL-1m is synthesized from MovieLens-1m which is representative benchmark dataset for sequential recommendation. -
RESACT: REINFORCING LONG-TERM ENGAGEMENT
Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and... -
Rethinking reinforcement learning for recommendation: A prompt perspective
A prompt-based approach for sequential recommendation. -
Local Policy Improvement for Recommender Systems
Recommender systems predict what items a user will interact with next, based on their past interactions. -
Amazon-Luxury
The dataset is used for sequential recommendation tasks, and it contains user-item interaction history. -
Amazon Custom Review Dataset
The dataset is used for sequential recommendation tasks, and it contains user-item interaction history. -
MovieLens100K
The dataset is used for sequential recommendation tasks, and it contains user-item interaction history. -
MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation
Sequential recommendation is a fundamental task for network applications, and it usually suffers from the item cold start problem due to the insufficiency of user feedbacks. -
CDs and Vinyl dataset
The CDs and Vinyl dataset is part of the updated Amazon Review Data. There are 129,237 users, 145,522 items, and 1,682,049 user behaviors in total. -
Movies and TV dataset
The Movies and TV dataset is part of the updated version of Amazon Review Data. There are 304,763 users, 89,590 items, and 3,506,470 user behaviors in total. -
Book dataset
The Book dataset is part of the Amazon Product Data in the "book" category. There are 603,668 users, 367,982 items, and 8,898,041 user behaviors in total.
