{"title":"Multi-corpus Personalized Recommendations on Google Play","authors":"L. Koc, C. Master","doi":"10.1145/2959100.2959129","DOIUrl":null,"url":null,"abstract":"Google Play is a seamless approach to digital entertainment on all of your devices. It gives you one place to find, enjoy and share your favorite entertainment, from apps to movies, music, books and more, on the web or any device. With more than 1 billion active users in 190+ countries around the world, Play is an important distribution platform for developers to build a global audience. More than 50 billion apps-have been downloaded from Google Play. However, generating personalized recommendations for different kind of content is a complex technical and product problem. Each of Play verticals (apps, games, books, movies, music) has different business goals, metrics to optimize, and user behavior. In this talk, we'll present an overview of how Play recommendations work across these verticals, how we evaluate our results, and the impact of deep neural networks in improving recommendations.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 10th ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2959100.2959129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Google Play is a seamless approach to digital entertainment on all of your devices. It gives you one place to find, enjoy and share your favorite entertainment, from apps to movies, music, books and more, on the web or any device. With more than 1 billion active users in 190+ countries around the world, Play is an important distribution platform for developers to build a global audience. More than 50 billion apps-have been downloaded from Google Play. However, generating personalized recommendations for different kind of content is a complex technical and product problem. Each of Play verticals (apps, games, books, movies, music) has different business goals, metrics to optimize, and user behavior. In this talk, we'll present an overview of how Play recommendations work across these verticals, how we evaluate our results, and the impact of deep neural networks in improving recommendations.
Google Play是你在所有设备上进行数字娱乐的无缝途径。它给你一个地方找到，享受和分享你最喜欢的娱乐，从应用程序到电影，音乐，书籍和更多，在网络或任何设备上。Play在全球190多个国家拥有超过10亿活跃用户，是开发者打造全球用户的重要分销平台。Google Play的应用程序下载量已经超过500亿次。然而，为不同类型的内容生成个性化推荐是一个复杂的技术和产品问题。每个Play垂直领域(游戏邦注:包括应用、游戏、书籍、电影和音乐)都有不同的商业目标、需要优化的指标和用户行为。在本次演讲中，我们将概述Play推荐如何在这些垂直领域中发挥作用，我们如何评估我们的结果，以及深度神经网络在改进推荐方面的影响。
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