Short Video Mobile Product Design Based On User Behavior Data Analysis

In recent years,the domestic Internet in the content field,the We Chat public number,today's headlines,interesting headlines and other information platforms shine.These Internet platforms make use of the ideas of the whole people and a series of incentives to close the loop,so that the number of content is greatly increased.Due to the popularity of 4g networks in China,the short video form occupies a very large proportion on the content side.Therefore,how to analyze the user's favorite content based on the user behavior data,and recommend the video content that the user is most interested in to the user is very important.The duration of short videos is generally within 5 minutes,most of them are within 2minutes.The short video is mainly transmitted on mobile terminals,and can be easily and quickly spread and spread on third-party social media.Compared with traditional long video,short video is smaller in size and lower in cost.In just tens of seconds to minutes,it can intuitively display the essence of content,satisfying the current user's rapid fragmentation time.The need to obtain content,so short video is currently a very important form of information dissemination.With the continuous iteration of intelligent terminals and the rapid development of the network,the short video will occupy a pivotal position when consumer users are fragmented.More companies are optimistic about this field,investing in this field,and carrying out research and development work in this area.Under the background of the rapid increase of short video content of major information platforms,it also takes into account the product form characteristics of short video itself.This paper proposes corresponding solutions from user interface design,R&D testing,content cold start filling,crawler system,content distribution,algorithm recommendation,data analysis,product iteration and so on.At the same time,this article will focus on the core user behavior data in short video,such as functional penetration rate,average number of views,average user watch time,short video retention rate,interaction rate and other core data for key research,its significance It is to enhance the user stickiness of the short video platform and the overall platform sustainability competitiveness.In addition,this paper will also study the improvement of short video data from various recommendation algorithms from the algorithm recommendation.Mainly based on the following three engines: new content recommendation engine,high quality recommendation engine,user collaborative recommendation engine.