DML

This is the pytorch implementation of our paper at CIKM 2023:

Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework

Yang Zhang, Yimeng Bai, Jianxin Chang, Xiaoxue Zang, Song Lu, Jing Lu, Fuli Feng, Yanan Niu, Yang Song.

Usage

⚠️ Due to company policies, the code associated with this paper is not publicly available.

However, the core component of DML—the WPR label (also known more broadly as Watch Time Distribution, or WTD)—can be viewed as an extension of D2Q, enhanced with adaptive binning. For reference, we encourage readers to consult the following D2Q implementations:

• Official D2Q Implementation
• Alternative D2Q Implementation

Citation

@inproceedings{10.1145/3583780.3615483,
author = {Zhang, Yang and Bai, Yimeng and Chang, Jianxin and Zang, Xiaoxue and Lu, Song and Lu, Jing and Feng, Fuli and Niu, Yanan and Song, Yang},
title = {Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework},
year = {2023},
isbn = {9798400701245},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583780.3615483},
doi = {10.1145/3583780.3615483},
booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages = {4952–4959},
numpages = {8},
keywords = {recommender system, debiasing, causal recommendation},
location = {Birmingham, United Kingdom},
series = {CIKM '23}
}