System and method of automatically searching hot point video

Abstract

The invention discloses a system and a device of automatically searching a hot point video. The system comprises a video information base, a population parameter statistics module, an increment statistics module and a hot point video computation module. The video information base stores a mass of video information. The population parameter statistics module is used for counting total statistical magnitude of each population parameter statistics item of each of the video information in the video information base. The increment statistics module is used for counting newly increased statistical magnitude of each increment statistics item of each of the video information in a particular time period. The hot point video computation module is used for calculating weight of each property of each of the video information, weight of each population parameter statistics item of the population parameter statistics module and weight of each increment statistics item of the increment statistics module by using each video-weight calculation model. Hot weight value of the video information is obtained through the hot point video computation module according to each weight result obtained by calculation. A hot point video list is selected to be displayed according to the hot weight value of the video information. The system and the device of automatically searching the hot point video is capable of automatically generating hot point video searching result through acquisition of clicking of a user.

Description

technical field [0001] The present invention relates to a hot video recommendation system and method, in particular to a hot video automatic mining system and method. Background technique [0002] Due to the high popularity of computers and the fast and unlimited nature of the Internet, the number of Internet users has increased significantly, forming an astonishing number of consumer groups. The demand and use of the Internet has become an indispensable part of the modern technological society. , making many industries optimistic about the huge business opportunities implied by the Internet, such as various types of e-commerce, online games, online financial transactions, language learning, and online videos. [0003] However, with the rapid development of video websites, it is increasingly difficult for users to find interesting videos from a large amount of irrelevant information. In this case, users often need video websites to recommend some high-quality videos from a l...

AI Technical Summary

Problems solved by technology

However, this method is time-consuming, low in efficiency, poor in accuracy, and relies on editors' personal taste and vision, which is not objective and often cannot reflect the real needs of users, so it is increasingly difficult to meet the needs of netizens for video website recommendations.

[0004] Another way to recommend high-quality videos from a large number of videos is to use the computer system to count the clicks of each video in the past period of time, dig out the videos that users welcome, mark them as popular videos, and recommend them to users. However, the disadvantage of this method is that , the hot video does not always refer to the video with the largest total number of clicks, because the total number of clicks is often generated during the long-term accumulation of the video, which does not necessarily mean that the video is currently hot

For example, the video of the 9.11 incident was once a hot spot at that time, so the total number of clicks on the video was very high, but it is obviously inappropriate to be recommended as a hot video today.

[0005] To sum up, it can be seen that the video recommendation method of the prior art has the problems of low efficiency, poor precision and failure to meet the needs of users. Therefore, it is necessary to propose improved technical means to solve this problem

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