My implementation for MINER: Multi-Interest Matching Network for News Recommendation. Please see the paper.
I run my code with Python==3.9, pytorch==1.11.0, transformers==4.21.0, scikit-learn==1.1.2, tensorboard==2.9.1,
tqdm==4.64.0.
The datasets used in my experiments have some files:
| File Name | Description |
|---|---|
| news.tsv | The information of news articles |
| behaviors.tsv | The click histories and impression logs of users |
| category2id.json | Mapping the category of news to id |
| user2id.json | Mapping the user to id |
The behaviors.tsv file contains the impression logs and users' news click histories. It has 5 columns divided by the tab symbol:
- Impression ID: The ID of an impression.
- User ID: The ID of a user.
- Time: The impression time with format "MM/DD/YYYY HH:MM:SS AM/PM".
- History: The news click history (ID list of clicked news) of this user before this impression. The clicked news articles are ordered by time.
- Impressions: List of news displayed in this impression and user's click behaviors on them (1 for click and 0 for non-click). The orders of news in an impressions have been shuffled.
The news.tsv file contains the detailed information of news articles involved in the behaviors.tsv file. It has 4 columns, which are divided by the tab symbol:
- News ID: The ID of a news
- Title: The title of a news
- Category: The category of a news
- Sapo: The abstract/summary of a news
In the category2id.json file and user2id.json file, I use the <unk> token to encode categories and users that are not in training set. In addition, the <pad> token is also added to those.
Download the data and save it in data/.
Single GPU training:
python main.py train @config/train.txtpython main.py eval @config/eval.txt --saved_model_path path_to_your_saved_model