Method and method for recommending data

A data recommendation and data technology, which is applied in video data retrieval, electronic digital data processing, special data processing applications, etc., can solve the problem of unable to realize personalized recommendation

Inactive Publication Date: 2014-06-18
SHENGLE INFORMATION TECH SHANGHAI
View PDF0 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, how to make personalized recommendations for new users without any click, watch or interaction behavior is a big problem
For these new users, the existing technology only selects popular data from the database to recommend them, but this recommendation method cannot achieve the purpose of personalized recommendation

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method and method for recommending data
  • Method and method for recommending data
  • Method and method for recommending data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037] like figure 1 As shown, the present invention provides a data recommendation method, including:

[0038] Step S11, using the features of all users and the number of the first data to be recommended as feature factors to train the weight factors of all users. Wherein, the data may be a video, and the serial number of the first data to be recommended may be a video ID, and different user characteristics represent different preferences of the data to be recommended.

[0039] Preferably, the characteristics of the user can be obtained from a user behavior log, and the characteristics of the user include browser type, display resolution, network device type, time of visiting the website, location, user website referrer and One or any combination of the user's landing page (landing page), the user's characteristics include one or more characteristic factors, of course, the user's characteristics can also include the data displayed by the user, the data clicked or watched by ...

Embodiment 2

[0065] like figure 2 As shown, the present invention provides another data recommendation method. The difference between this embodiment and the embodiment is that the first data to be recommended is high-quality data obtained regularly, and the high-quality data is classified according to the characteristics of one or more users. Sorting, obtaining the top Q high-quality data as the second recommendation data, so as to make the recommendation result more accurate, the method includes:

[0066] In step S21, the regularly acquired high-quality data is used as the first data to be recommended; specifically, the data can be videos, for example, high-quality videos can be regularly obtained from a video library of the entire network and updated and stored in a high-quality video library. There may be tens of millions or even hundreds of millions of videos in the online video library, and the amount of data is too large. It will be a lot of work to train the weight factors of all ...

Embodiment 3

[0095] like Figure 4 As shown, the present invention also provides a data recommendation system, including a model module 1 and a recommendation engine module 2 . Wherein, the data may be a video, and the serial number of the first data to be recommended may be a video ID.

[0096] The model module 1 is used to use the features of all users and the number of the first data to be recommended as feature factors to train the weight factors of all users.

[0097] Preferably, the number of weighting factors=1+the number of features of all users×the number of data to be recommended.

[0098] Preferably, the characteristics of the user can be obtained from a user behavior log, and the characteristics of the user include browser type, display resolution, network device type, time of visiting the website, location, user website referrer and One or any combination of the user's landing page (landing page), the user's characteristics include one or more characteristic factors, of cour...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a method and system for recommending data, wherein the method comprises the steps of training weight factors of all users by taking the characteristics of all the users and the serial numbers of pieces of first to-be-recommended data as characteristic factors, obtaining the pieces of second to-be-recommended data from the pieces of first to-be-recommended data and obtaining predicted click rates of the pieces of second to-be-recommended data corresponding to a user requesting recommendation according to the weight factors, the characteristic of the user requesting recommendation and the serial numbers of the pieces of second to-be-recommended data, ranking the predicted click rates from high to low, obtaining first K pieces of the second to-be-recommended data highest in the predicted click rate and recommending the obtained first K pieces of the second to-be-recommended data to the user requesting recommendation, wherein K is a positive integer. The method and the system are capable of realizing personalized recommendation to a new user by thoroughly utilizing the characteristics of the user and the characteristic factors carried in the to-be-recommended data, and the characteristic factors are quite easy to extend, and therefore, new weight factors can be trained quickly according to new characteristic factors.

Description

technical field [0001] The invention relates to a data recommendation method and system. Background technique [0002] The personalized recommendation of data, such as the personalized recommendation of a video website, is often based on the interaction between the user and the data, such as video, to recommend to the user the data that the user is interested in, such as video. [0003] However, how to make personalized recommendations for new users without any click, watch or interaction behavior is a big problem. For these new users, the existing technology only selects popular data from the database to recommend them, but this recommendation method cannot achieve the purpose of personalized recommendation. Therefore, there is an urgent need for a personalized recommendation method and system for new users. Contents of the invention [0004] The purpose of the present invention is to provide a data recommendation method and system, which can make full use of the user's...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/74G06F16/9535
Inventor 纪达麒陈运文刘作涛辛颖伟王文广姚璐邹溢
Owner SHENGLE INFORMATION TECH SHANGHAI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap