ALSTP

A pytorch GPU implementation of ALSTP.

Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Yinglong Wang, Junma and Mohan Kankanhalli (2018). Attentive Long Short-Term Preference Modeling for Personalized Product Search. In TOIS.

Please cite our TOIS paper if you use our codes. Thanks!

You can download the Amazon Dataset from http://jmcauley.ucsd.edu/data/amazon.

The requirements are as follows:

* python==3.6
* pandas==0.24.2
* numpy==1.16.2
* pytorch==0.4.1
* gensim==3.7.1
* tensorboardX==1.6

Example to Run

• Make sure the raw data, meta data are in the same direction.
• Preprocessing data. Filter the review to each user having at least 10 transactions. Remove the words whose number is less than count. Split the data into three sets and extract queries.

  python scripts/process.py --review_file=selected_file --meta_file=selected_file --count=5
  

• We leverage the PV-DM model to convert queries and product representations to the same latent space.

  python scripts/doc2vec.py --window_size=3
  

• Start training the model.

  python ALSTP.py --lr=0.001 --num_steps=4 --alpha=0.9