# CS3319-01 Project 2: Recommendation 2024 Spring — Data

来源：https://www.kaggle.com/competitions/cs3319-01-project-2-recommendation-2024-spring/data

采集时间：2026-10-19T10:00:34Z

File descriptions
SampleSubmission.csv
- The sample submission, you can check the format of expected output.
Columns:

1. `Index` - the row index of test set.
2. `Predicted` - the predicted results (recommandation or not) of the author reading the paper. Range: {0, 1}.
bipartite_train_ann.txt
- Contains an anonymous bipartite network with two integers per line (an edge). The first id refers to an author, and the second id refers to a paper that the author cites.
Columns:

1. `author` - The author id.
2. `paper` - The paper id that cited by the author.
bipartite_test_ann.txt
The list of author-paper pairs that need to be predicted(a pair each row), and it's recommended to output your prediction in the order of pairs in this file.
(Test set)
Columns: similar to that of
bipartite_train_ann.txt
.
author_file_ann.txt
- Contains an anonymous coauthor network with two integers (range from 0 to 6610, each represents one unique author) per row (an edge), which means two authors have cooperated.
Columns:

1.2. `author` - The author id that have collaborated.
paper_file_ann.txt
- Contains an anonymous citation network with two integers (range from 0 to 79936, each represents one unique paper) per row (an edge), referring that the paper with former id cites the paper with latter id.
Columns:

1. `paper` - The paper id.
2. `paper` - The id of cited paper.
feature.pkl
- For a better and reasonable experiment setup, we provide initial features with a dimension size of 512 for each paper, generated by USE ([Universal Sentence Encoder](assets/dd7361edb0e73d4f5c9843f7.pdf)), and need to be read with pickle.load() .
In Kaggle, you just need to submit your prediction result in '.csv' format, but pay attention to other materials that need to be submitted on the canvas platform.