Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021

Last update: Nov 22, 2022

Related tags

Recommender Systems recommender-systems information-bottleneck-theory graph-neural-networks aaai2021 l0-regularization feature-interactions

Overview

Detecting Beneficial Feature Interactions for Recommender Systems (L0-SIGN)

This is our implementation for the paper:

Su, Y., Zhang, R., Erfani, S., & Xu, Z. (2021). Detecting Beneficial Feature Interactions for Recommender Systems. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI). link1 or Link2

Description

Feature interactions are essential for achieving high accuracy in recommender systems. Many studies take into account the interaction between every pair of features. However, this is suboptimal because some feature interactions may not be that relevant to the recommendation result, and taking them into account may introduce noise and decrease recommendation accuracy. To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy. The automatic feature interaction detection is achieved via edge prediction with an L0 activation regularization. Our proposed model is proved to be effective through the information bottleneck principle and statistical interaction theory.

Figure2: An Overview of the L0-SIGN Model.

What are in this Repository

This repository contains the following contents:

/
├── code/                   --> (The folder containing the source code)
|   ├── dataloader.py       --> (The code to proceed the data into code-usable format)
|   ├── SIGN_main.py             --> (The main code file. The code is run through this file)
|   ├── SIGN_model.py            --> (Contains the function of our GMCF model.)
|   ├── SIGN_train.py            --> (Contains the code to train and evaluate our GMCF model.)
├── data/                   --> (The folder containing three used datasets)   
|   ├── frappe/             --> (The frappe dataset to evaluate recommendation.)
|   ├── ml-tag/             --> (The MovieLens Tag dataset to evaluate recommendation.)
|   ├── twitter/            --> (The Twitter dataset to evaluate graph classification.)
|   ├── DBLP_v1/            --> (The DBLP dataset to evaluate graph classification.)
├── img/                    --> (The images for README (not used for the code))   
|   ├── SIGN_frame.png      --> (The overall structure of our L0-SIGN model)
├── LICENCE                 --> (The licence file)

Run our code

To run our code, please follow the instructions in our code/ folder.

Cite our paper

Please credit our work by citing the following paper:

@inproceedings{su2021detecting,
  title={Detecting Beneficial Feature Interactions for Recommender Systems},
  author={Su, Yixin and Zhang, Rui and Erfani, Sarah and Xu, Zhenghua},
  booktitle={Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI)},
  year={2021}
}

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Comments

Experimental setup for comparison with your work

Dear researchers, I read your paper and it is so interesting. However, I would try to reproduce your experiments and I have on question: I run your code but just achieve the results(auc:0.8942, acc:0.8855 on movie-tags) with 300 epochs, i want to know how to achieve the experiment data in your paper.

Thank you very much, wangzhen38

opened by wangzhen38 1
Experimental setup for comparison with baseline

This is an interesting work. When I realized the baseline of Frappe data set, I found that the evaluation metrics were better than those reported in this paper. I want to know whether your baseline hyperparameter setting is consistent with the model proposed in this paper or whether grid search is the best.

opened by Flyingkeyboard699 2

Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021

Related tags

Overview

Detecting Beneficial Feature Interactions for Recommender Systems (L0-SIGN)

Description

What are in this Repository

Run our code

Cite our paper

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Comments

Experimental setup for comparison with your work

Experimental setup for comparison with baseline

Owner

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