Deep Learning for Next Basket Recommendation

This repository contains my implementations of DREAM for next basket prediction.

Requirements

• Python 3.6
• Pytorch 1.6.0
• Pandas 1.1.2
• Sklearn 0.19.1
• Numpy 1.16.2
• Gensim 3.5.0
• Tqdm 4.49.0

Data

You can download the Negative Sample (neg_sample.pickle) used in code. Make sure they are under the /data folder.

Data Format

See data format in data folder which including the data sample files.

This repository can be used in other e-commerce datasets in two ways:

1. Modify your datasets into the same format of the sample.
2. Modify the data preprocess code in data_helpers.py.

Anyway, it should depend on what your data and task are.

Network Structure

DREAM uses RNN to capture sequential information of users' shopping behavior. It extracts users' dynamic representations and scores user-item pair by calculating inner products between users' dynamic representations and items' embedding.

The framework of DREAM:

1. Pooling operation on the items in a basket to get the representation of the basket.
2. The input layer comprises a series of basket representations of a user.
3. The dynamic representation of the user can be obtained in the hidden layer.
4. The output layer shows scores of this user towards all items.

References:

Yu, Feng, et al. "A dynamic recurrent model for next basket recommendation." Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2016.

About Me

黄威，Randolph

SCU SE Bachelor; USTC CS Ph.D.

Email: chinawolfman@hotmail.com

My Blog: randolph.pro

LinkedIn: randolph's linkedin