Item-based Variational Auto-encoder for Fair Music Recommendation

• URL: http://arxiv.org/abs/2211.01333v1
• Date: Mon, 24 Oct 2022 06:42:16 GMT
• Title: Item-based Variational Auto-encoder for Fair Music Recommendation
• Authors: Jinhyeok Park, Dain Kim, Dongwoo Kim
• Abstract summary: 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)
• Score: 1.8782288713227568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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