自动播放列表延续的混合两阶段推荐系统

V. Rubtsov, Mikhail Kamenshchikov, I. Valyaev, V. Leksin, D. Ignatov
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引用次数: 9

摘要

在本文中,我们为我们的Avito团队提供了RecSys挑战赛2018的解决方案,该团队获得了主赛道第三名的成绩。比赛的目标是为自动播放列表推荐音乐曲目。作为这项挑战的一部分,Spotify发布了一个大型公共数据集,这使我们能够训练一个相当复杂的算法。我们的方法包括两个阶段:用于候选选择的协同过滤和用于最终预测的梯度增强。这两种模型的组合在给定的播放列表和曲目元数据下表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid two-stage recommender system for automatic playlist continuation
In this paper, we provide the solution for RecSys Challenge 2018 by our Avito team, which obtained the 3rd place in main track. The goal of the competition was to recommend music tracks for automatic playlist continuation. As a part of this challenge, Spotify released a large public dataset, which allowed us to train a rather complex algorithm. Our approach consists of two stages: collaborative filtering for candidate selection and gradient boosting for final prediction. The combination of these two models performed well with the playlist and track metadata given.
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