Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework

TL;DR

Track2Vec is a GPU-free framework for fair music recommendation that balances accuracy and fairness through customizable modules and introduces a novel fairness metric, achieving top leaderboard performance.

Contribution

The paper presents a novel GPU-free, customizable framework for fair music recommendation, incorporating a new fairness metric and ensemble approach for improved results.

Findings

Achieved 4th place in EvalRS @ CIKM 2022 challenge.

Outperformed baseline by approximately 200% in scores.

Ensembling modules enhances both fairness and accuracy.

Abstract

Recommendation systems have illustrated the significant progress made in characterizing users' preferences based on their past behaviors. Despite the effectiveness of recommending accurately, there exist several factors that are essential but unexplored for evaluating various facets of recommendation systems, e.g., fairness, diversity, and limited resources. To address these issues, we propose Track2Vec, a GPU-free customizable-driven framework for fairness music recommendation. In order to take both accuracy and fairness into account, our solution consists of three modules, a customized fairness-aware groups for modeling different features based on configurable settings, a track representation learning module for learning better user embedding, and an ensemble module for ranking the recommendation results from different track representation learning modules. Moreover, inspired by TF-IDF which has been widely used in natural language processing, we introduce a metric called Miss Rate - Inverse Ground Truth Frequency (MR-ITF) to measure the fairness. Extensive experiments demonstrate that our model achieves a 4th price ranking in a GPU-free environment on the leaderboard in the EvalRS @ CIKM 2022 challenge, which is superior to the official baseline by about 200% in terms of the official scores. In addition, the ablation study illustrates the necessity of ensembling each group to acquire both accurate and fair recommendations.