用户对电影解说视频喜好等级分层数据

在日常生活工作之余，越来越多的人喜欢刷视频来缓解压力，有部分人群对电影解说视频感兴趣。统计分析用户观看电影解说视频的数据，通过对历史观看用户建立表格，对用户进行标签制定，定位用户喜好级别，通过大数据推广，为用户推送符合喜好的视频提供数据支持。1. 数据采集:采集用户对电影解说视频的点击数量等信息; 2. 用户点击数量占总点击数量的比例=用户点击数量/总点击数量*100%； 3. 用户点击数量占总点击数量的比例按从大到小进行排名；分类运用ABCDEF分类法，对占比0.26%以上的，给予“A类用户”分层；占比0.21%到0.25%区间的，则给予“B类用户”分层；占比在0.16%到0.20%区间，则给予“C类用户”分层；占比在0.11%到0.15%区间，则给予“D类用户”分层；占比在0.06%到0.1%区间，则给予“E类用户”分层；占比在0.05%以下，则给予“F类用户”分层； 4. 数据应用: 电影解说视频的点击次数结果可以作为用户喜好等级评价的依据，为监管部门向监管提供技术支撑。

In their spare time away from daily work and life, an increasing number of people prefer watching short videos to relieve stress, and a subset of these users are interested in movie explanation videos. This work statistically analyzes user viewing data for movie explanation videos, compiles a tabular dataset of historical viewers, formulates user tags, and positions users' preference levels, providing data support for pushing targeted videos matching users' preferences via big data promotion.
1. Data Collection: Collect information such as the number of clicks users generate on movie explanation videos;
2. Proportion of user clicks in total clicks = (Number of user clicks / Total number of clicks) × 100%;
3. Rank the calculated proportions of user clicks in total clicks in descending order. Apply the ABCDEF classification method for user stratification: users with a proportion above 0.26% are categorized as "Category A Users"; those with a proportion ranging from 0.21% to 0.25% are "Category B Users"; those with a proportion between 0.16% and 0.20% are "Category C Users"; those with a proportion between 0.11% and 0.15% are "Category D Users"; those with a proportion between 0.06% and 0.10% are "Category E Users"; and those with a proportion below 0.05% are "Category F Users";
4. Data Application: The click count results of movie explanation videos can serve as the basis for evaluating users' preference levels, providing technical support for regulatory authorities to carry out their supervision work.

搜集汇总

数据集介绍

特点

该数据集记录了用户对电影解说视频的点击行为，包含690条数据，每年更新一次。通过分析用户点击占比，将用户分为A-F六个等级，用于支持个性化视频推送和用户喜好分析。