PURS

This is our implementation for the paper:

Pan Li, Maofei Que, Zhichao Jiang, Yao Hu and Alexander Tuzhilin. "PURS: Personalized Unexpected Recommender System for Improving User Satisfaction." Proceedings of the 14th ACM Conference on Recommender Systems. 2020. [Paper]

Dataset: [Yelp dataset] [MovieLens Dataset] Due to the confidential agreement with the Youku company, we are not allowed to make the Youku dataset publicly available. Nevertheless, you are always welcome to use our codes for the two public datasets and your own dataset.

Please cite our RecSys'20 paper if you use our codes. Thanks!

Author: Pan Li (https://lpworld.github.io/)

Environment Settings

We use Tensorflow as the backend.

• Tensorflow version: '1.4.0'

Example to run the codes.

The instruction of commands has been clearly stated in the codes (see the parse_args function).

Run PURS:

python train.py

Acknowledgement

This implementation is inspired from Deep Interest Network.

Last Update: 2020/07/27