博客笔记一: [Netflix] Data Science @ Netflix & Promoting Netflix Originals!

本文介绍了一种使用Generalized Mixed Effect (GMM) 模型和Gradient Boosting Trees (GBT) 的方法,通过预发布社交信号预测观众规模。此外,我们正在扩大对自然语言处理和机器学习的应用范围,以分析大量社交行为数据,并为好莱坞的创意合作伙伴创造工具。

之前喜欢用笔来记,现在发现效率有点低,放在csdn上吧。
Kelly Uphoff
https://www.linkedin.com/pulse/data-science-netflix-promoting-originals-kelly-uphoff/?lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_post_details%3Bxzhfbq0eS1u%2BfZY4fSlayA%3D%3D

We leverage techniques like Generalized Mixed Effect (GMM) Models or Gradient Boosting Trees (GBM) to use pre-launch social signals and predict audience sizes.

We are expanding our use of natural language processing and machine learning to churn through tons of our own social and behavioral data and create tools for our creative partners in Hollywood.

著名的Netflix 智能推荐 百万美金大奖赛使用是数据集. 因为竞赛关闭, Netflix官网上已无法下载. Netflix provided a training data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Each training rating is a quadruplet of the form . The user and movie fields are integer IDs, while grades are from 1 to 5 (integral) stars.[3] The qualifying data set contains over 2,817,131 triplets of the form , with grades known only to the jury. A participating team's algorithm must predict grades on the entire qualifying set, but they are only informed of the score for half of the data, the quiz set of 1,408,342 ratings. The other half is the test set of 1,408,789, and performance on this is used by the jury to determine potential prize winners. Only the judges know which ratings are in the quiz set, and which are in the test set—this arrangement is intended to make it difficult to hill climb on the test set. Submitted predictions are scored against the true grades in terms of root mean squared error (RMSE), and the goal is to reduce this error as much as possible. Note that while the actual grades are integers in the range 1 to 5, submitted predictions need not be. Netflix also identified a probe subset of 1,408,395 ratings within the training data set. The probe, quiz, and test data sets were chosen to have similar statistical properties. In summary, the data used in the Netflix Prize looks as follows: Training set (99,072,112 ratings not including the probe set, 100,480,507 including the probe set) Probe set (1,408,395 ratings) Qualifying set (2,817,131 ratings) consisting of: Test set (1,408,789 ratings), used to determine winners Quiz set (1,408,342 ratings), used to calculate leaderboard scores For each movie, title and year of release are provided in a separate dataset. No information at all is provided about users. In order to protect the privacy of customers, "some of the rating data for some customers in the training and qualifyin
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值