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Method and method for recommending data

A data recommendation and data technology, applied in the direction of electronic digital data processing, special data processing applications, instruments, etc., can solve the problems of not being able to provide corresponding services for different users' hobbies and loss of consumers, so as to reduce invalid browsing time and save Traffic costs, the effect of improving reading satisfaction

Inactive Publication Date: 2014-06-18
SHENGLE INFORMATION TECH SHANGHAI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This process of browsing a large amount of irrelevant information and products will undoubtedly drown consumers in the problem of information overload, resulting in continuous loss of consumers
[0003] With the rapid development of Internet technology, a large amount of information is presented to users at the same time. Traditional search can only present the same sorting results to all users, and cannot provide corresponding services for different users' interests and hobbies.

Method used

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  • Method and method for recommending data

Examples

Experimental program
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Effect test

Embodiment 1

[0040] like figure 1 As shown, the present invention provides a data recommendation method, including:

[0041] Step S11, according to the user's historical behavior on the data, all the likes and dislikes data of the user and the likes and dislikes of the user for all the likes and dislikes data are obtained.

[0042] Preferably, the likes and dislikes data include explicit likes and dislikes data and implicit likes and dislikes data, and the explicit likes and dislikes data may include a user buying a certain product, rating a certain movie, or collecting a certain music Or explicitly express dislike of something, etc., the implicit likes and dislikes data may include a user has browsed a certain product, clicked on a certain music, the user's social relationship, or indirectly expressed dislike of a certain thing. According to the likes and dislikes data of different users, such as favorite items, different recommendation results will be obtained in the subsequent steps, a...

Embodiment 2

[0073] 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 merging interest degree of merging the same neighbor data in all dimensions is added to obtain the second merging of each neighbor data by the user. Degree of interest, and the step of sorting the second merged interest degree of all neighbor data from high to low, obtaining the first N neighbor data with the highest second merged interest degree and recommending to the user, so as to optimize the recommendation result, including :

[0074] Step S21, according to the user's historical behavior on the data, all the likes and dislikes data of the user and the likes and dislikes of the user for all the likes and dislikes data are obtained.

[0075] Preferably, the likes and dislikes data include explicit likes and dislikes data and implicit likes and dislikes data, and the explicit likes and dislikes data m...

Embodiment 3

[0110] like image 3 As shown, the present invention also provides a data recommendation system, including a user history behavior module 1 , a neighbor feedback module 2 , a user interest node module 3 and a personalized recommendation module 4 .

[0111] The user historical behavior module 1 is used to obtain all the likes and dislikes data of the user and the likes and dislikes of the user to all the likes and dislikes data according to the user's historical behavior on the data.

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Abstract

The invention relates to a method and system for recommending data, wherein the method comprises the steps of obtaining the like and dislike degrees of a user to all like and dislike data according to historical behaviors of the user to data, containing the relevancy of each piece of like and dislike data with neighboring data thereof in a preset pattern, obtaining a first combination interestingness of the user to each piece of neighboring data according to the like and dislike degree of the user to each piece of like and dislike data and the relevancy of each piece of like and dislike data with the neighboring data thereof under each predetermined dimension, combining the first combination interestingnesses of the same neighboring data under all dimensions to obtain a second combination interestingness of the user to each piece of neighboring data , and obtaining first N neighboring data highest in the second combination interestingness and recombining the obtained first N neighboring data to the user. The method and the system enable the recommendation result to change according to different historical behaviors of the user every day; in other words, once the taste of the user is changed, the recommendation result also changes, and therefore, the user is enabled to enjoy excellent personalized experience.

Description

technical field [0001] The invention relates to a data recommendation method and system. Background technique [0002] Personalized recommendation is based on the user's interest characteristics and purchase behavior, recommending data such as information and products that the user is interested in to the user. With the continuous expansion of the scale of online commodities, the number and types of commodities are also increasing rapidly, so customers need to spend a lot of browsing time to find the commodities they want to buy. This process of browsing a large amount of irrelevant information and products will undoubtedly drown consumers in the problem of information overload, resulting in continuous loss of consumers. [0003] With the rapid development of Internet technology, a large amount of information is presented to users at the same time. Traditional search can only present the same sorting results to all users, and cannot provide corresponding services for differ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/9535
Inventor 辛颖伟陈运文纪达麒刘作涛姚璐邹溢王文广
Owner SHENGLE INFORMATION TECH SHANGHAI
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