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

Wei Wei Du*, Wei Yao Wang, Wen Chih Peng


研究成果: Conference article同行評審


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.

期刊CEUR Workshop Proceedings
出版狀態Published - 2022
事件2022 International Conference on Information and Knowledge Management Workshops, CIKM-WS 2022 - Atlanta, United States
持續時間: 17 10月 202221 10月 2022


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