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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... -
Amazon Cell Phones and Accessories 5-core
The dataset includes 27,879 users (sequences), 10,429 items, and 194,439 ratings with density as 0.06%. -
Amazon Beauty 5-core
Sequential Recommendation characterizes the evolving patterns by modeling item sequences chronologically. The essential target of it is to capture the item transition correlations. -
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. -
DiffuRec: A Diffusion Model for Sequential Recommendation
DiffuRec: A Diffusion Model for Sequential Recommendation -
MovieLens Latest, MovieLens 1m, MovieLens 10m, Yelp
The dataset used in the paper is MovieLens Latest, MovieLens 1m, MovieLens 10m, and Yelp. -
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.
