论文标题
艺术家驱动的分层和用户的行为对播放列表持续场景中的建议影响
Artist-driven layering and user's behaviour impact on recommendations in a playlist continuation scenario
论文作者
论文摘要
在本文中，我们概述了我们用作2018年ACM Recsys Challenge的团队奶油萤火虫的方法。由Spotify组织的竞赛重点是播放列表延续的问题，这表明该跟踪用户可能会添加到现有的播放列表中。挑战在许多用例中都解决了这个问题，从播放列表冷启动到已经由多达一百个曲目组成的播放列表。我们的团队根据一些基于内容和协作的模型提出了一个解决方案，其预测是通过结合步骤汇总的。此外，通过分析数据的基本结构，我们提出了一系列的提升，将在最终预测的基础上应用并提高建议质量。所提出的方法利用著名的算法，并能够提供高建议质量，同时需要有限的计算资源。
In this paper we provide an overview of the approach we used as team Creamy Fireflies for the ACM RecSys Challenge 2018. The competition, organized by Spotify, focuses on the problem of playlist continuation, that is suggesting which tracks the user may add to an existing playlist. The challenge addresses this issue in many use cases, from playlist cold start to playlists already composed by up to a hundred tracks. Our team proposes a solution based on a few well known models both content based and collaborative, whose predictions are aggregated via an ensembling step. Moreover by analyzing the underlying structure of the data, we propose a series of boosts to be applied on top of the final predictions and improve the recommendation quality. The proposed approach leverages well-known algorithms and is able to offer a high recommendation quality while requiring a limited amount of computational resources.
