Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

Overview

Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

This repository provides the source code of "Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering" accepted in TheWebConf (WWW2022) as a research paper.

1. Overview

We present ConCF Framework that exploits the complementarity from heterogeneous learning objectives throughout the training process, generating a more generalizable model.

In this work, we use five learning objectives for OCCF that have been widely adopted in recent work.

  • CF-A: Bayesian Personalized Ranking (BPR) Loss
  • CF-B: Collaborative Metric Learning (CML) Loss
  • CF-C: Binary Cross-Entropy (BCE) Loss
  • CF-D: Mean Squared Error (MSE) Loss
  • CF-E: Multinomial Likelihood Loss

2. Main Results

2-a. SingleCF vs. ConCF

  • The performance of the head trained by CF-A is significantly improved in ConCF.

  • The consensus collaboratively evolves with the heads based on their complementarity, providing accurate supervision..

    image

2-b. ConCF (w/o CL) vs. ConCF

  • ConCF (w/o CL) cannot effectively improve the performance of each head.

    image

3. Requirements

  • Python version: 3.6.10
  • Pytorch version: 1.5.0

4. How to Run

Please refer to 'Guide to using ConCF.ipynb' file.
Owner
SeongKu-Kang
Paper review: https://seongku.tistory.com/
SeongKu-Kang
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