Social relationship recommendation method

A recommendation method and social relationship technology, applied in the field of social relationship recommendation, can solve the problems of inconvenient recommendation, inconvenient data integration, large information, etc., and achieve the effect of saving time

Inactive Publication Date: 2018-09-28
上海同砚信息科技有限公司
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AI-Extracted Technical Summary

Problems solved by technology

However, this recommendation method is inconvenient to integrate the collected data, resulting in large and disorganized informat...
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Method used

In the present embodiment, in S4, when collecting social attention degree data, target user should be collected according to common good friend ratio and interaction situation, in S1, when user's data is collected, need according to user Browsing records and commonly used keywords are collected. In S1, when collecting user social attention data, first collect user comments, reposts, and favorite information, and then process the collected information to eliminate useless duplicate information. , in S6, when screening the information, first make the information into a table, compare the table with the user's data, and filter out the data with a large difference. When collecting the data of the int...
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Abstract

The invention discloses a social relationship recommendation method, comprising the following steps: S1, carrying out data collection, namely acquiring data of a user, integrating and classifying thedata, and analyzing the data, so that interestingness of the user and social contact attention are obtained; S2, carrying out data searching, namely searching similar data according to the interestingness of the user in the step S1, and carrying out primary screening on the searched data, so that data of target users is obtained; and S3, carrying out data comparison, namely comparing the screeneddata with the data of the user, recording a comparison result, and then preliminarily sorting the searched target users according to interest similarity. The method disclosed by the invention carriesout screening on the collected information for multiple times, the information is sorted, and useless repeated information is eliminated, so that the collected information is more accurate and ordered, the target users can be obtained more accurately and rapidly, and time is saved.

Application Domain

Data processing applicationsSpecial data processing applications

Technology Topic

Data harvestingCollections data +5

Examples

  • Experimental program(1)

Example Embodiment

[0022] Example
[0023] The social relationship recommendation method proposed in this embodiment includes the following steps:
[0024] S1: Data collection: Obtain user data, integrate and classify the data, and analyze the data to obtain user interest and social attention;
[0025] S2: Data search: Search for similar data according to the user's interest level described in S1, and perform preliminary screening on the searched data to obtain target user data;
[0026] S3: Data comparison: Compare the filtered data described in S2 with the user's data, and record the comparison results, and then sort the searched target users based on interest similarity;
[0027] S4: Social attention: According to the user's attention information, collect data of target users similar to the user's attention information, and record the data;
[0028] S5: Data comprehensive analysis: Integrate the target user interest and attention obtained in S3 and S4, and conduct a comprehensive analysis to filter the data again;
[0029] S6: Information screening: final screening of the information after screening described in S5 to obtain the final information;
[0030] S7: Information push: compare the final information described in S6 with the user's data again, sort according to the similarity with the user's data, and push the sorted information to the user;
[0031] S8: Information verification: The user sends a verification request to the target user based on the pushed data described in S7. When sending the verification request, the user’s basic data is also pushed to the target user. After the target user’s consent is obtained, the user can contact the target user. Users establish social relationships.
[0032] In this embodiment, in S4, when collecting social attention data, the target user should be collected based on the proportion of mutual friends and the interaction situation. In S1, when collecting user data, it needs to be based on the user's browsing history. And commonly used keywords are collected. In S1, when collecting user social attention data, first collect user comments, forwarding and favorite information, and then process the collected information to eliminate useless duplicate information. In S6 , When screening information, first make the information into a table, compare the table with the user’s data, and filter out data with large differences in data. When collecting data on interaction, the proportion of interaction should be counted, and Sort the statistical data. In S7, when sorting, the similarity is obtained according to the interest and attention, and the similarity is sorted from high to low. In S2, the searched data is initially filtered It is necessary to eliminate repetitive useless and incomplete information. The beneficial effect of the present invention is to filter the collected information multiple times and sort the information to eliminate useless repetitive information to make the collected information more accurate and effective. It can get the target users more accurately and quickly, saving time.

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Description & Claims & Application Information

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Classification and recommendation of technical efficacy words

  • shorten the time
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