/Quality-Aware-Neural-Complementary-Item-Recommendation

Primary LanguagePythonMIT LicenseMIT

Quality-Aware Neural Complementary Item Recommendation

This is our TensorFlow implementation for the paper:

@inproceedings{zhang2018quality, title={Quality-aware neural complementary item recommendation}, author={Zhang, Yin and Lu, Haokai and Niu, Wei and Caverlee, James}, booktitle={Proceedings of the 12th ACM Conference on Recommender Systems}, pages={77--85}, year={2018}, organization={ACM} }

Quick Start

Dataset is from McAuley, Julian, et al. "Image-based recommendations on styles and substitutes." Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2015.

Requirement:

  • Python 2.7
  • Tensorflow 1.1.0

First get the product image, textual and rating information and store them in .pickle. Format please see the code.

A quick way to use the model is:

python CompleRec.py