A Video Evaluation and Recommendation System

Abstract

The invention provides a video evaluation and recommendation system, which comprises a mode selection module, a video evaluation module, a video recommendation module, a self-made content evaluation and recommendation database and a self-made content history database. Wherein, a mode selection module is used for acquiring an evaluation mode instruction or a recommendation mode instruction issued by a user terminal; the video evaluation module is used for calling the data of the self-made content evaluation recommendation database and the standard data of the self-made content history databaseaccording to the evaluation mode instruction to evaluate the evaluation object and sending the evaluation result to the user terminal; the video recommendation module is used for calling the data of the self-made content evaluation recommendation database and the standard data of the self-made content history database according to the recommendation mode instruction, calculating and obtaining theself-made content which meets the filtering condition, and sending the filtering recommendation result to the user terminal.

Description

technical field [0001] The invention relates to the field of big data processing, in particular to a video evaluation and recommendation system. Background technique [0002] According to CNNIC's 41st statistical report on China's Internet development in 2017, among the 772 million Internet users in China, 578 million Internet users use Internet video applications, and the utilization rate of video applications reaches 75%. Ranked 4th. Video content has become an indispensable content in the online entertainment activities of Chinese Internet users. [0003] As the utilization rate of video applications continues to increase, video websites are actively seeking breakthroughs in the copyright purchase model facing increasingly fierce competition. Therefore, Internet self-made content comes into being, and Internet self-made content can play a dominant role in content selection, shooting, and broadcasting in video media. In 2017, a total of 686 self-made Internet content pr...

AI Technical Summary

Problems solved by technology

[0005] Therefore, brand advertisers will have many problems in the delivery of video content, such as: 1. The industry lacks a unified standard for online video evaluation, making it difficult to conduct targeted quantitative evaluation; 2. Phenomenon-level online video content is difficult to identify and cannot be guaranteed 3. It is impossible to judge whether the online video content meets the advertiser's own marketing goals and needs

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