论文标题
基于项目的变异自动编码器公平音乐推荐
Item-based Variational Auto-encoder for Fair Music Recommendation
论文作者
论文摘要
我们介绍了评估datachallenge的解决方案。评估datachallenge旨在考虑准确性，公平性和评估多样性，建立更现实的推荐系统。我们提出的系统基于基于项目的变分自动编码器（VAE）与贝叶斯个性化排名矩阵分解（BPRMF）之间的合奏。为了减轻流行的偏见，我们为每个受欢迎程度组使用一个基于物品的VAE，并具有额外的公平正规化。为了提出合理的建议，即使预测不准确，我们将推荐的BPRMF和基于项目的VAE的列表结合在一起。通过实验，我们证明了与基于用户的VAE相比，具有公平正则化的基于项目的VAE会大大降低受欢迎程度的偏差。基于项目的VAE和BPRMF之间的合奏使得前1项类似于地面真相，即使预测不准确。最后，我们根据我们从广泛的实验中的反思来提出一种“基于系数的方差公平”作为一种新的评估度量。
We present our solution for the EvalRS DataChallenge. The EvalRS DataChallenge aims to build a more realistic recommender system considering accuracy, fairness, and diversity in evaluation. Our proposed system is based on an ensemble between an item-based variational auto-encoder (VAE) and a Bayesian personalized ranking matrix factorization (BPRMF). To mitigate the bias in popularity, we use an item-based VAE for each popularity group with an additional fairness regularization. To make a reasonable recommendation even the predictions are inaccurate, we combine the recommended list of BPRMF and that of item-based VAE. Through the experiments, we demonstrate that the item-based VAE with fairness regularization significantly reduces popularity bias compared to the user-based VAE. The ensemble between the item-based VAE and BPRMF makes the top-1 item similar to the ground truth even the predictions are inaccurate. Finally, we propose a `Coefficient Variance based Fairness' as a novel evaluation metric based on our reflections from the extensive experiments.
