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Recommendation information output method, system and server

A recommended information and server technology, applied in the network field, can solve the problems of mismatching recommended information and recommended time, heavy workload of manual operation, etc., achieve the effect of reducing information input, increasing transmission speed, and overcoming low degree of automation

Active Publication Date: 2017-03-01
ALIBABA GRP HLDG LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the embodiment of the present application is to provide a method, system and server for outputting recommendation information, so as to solve the problems in the prior art that the output recommendation information does not match the recommendation time, and the manual operation causes a large workload

Method used

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  • Recommendation information output method, system and server
  • Recommendation information output method, system and server
  • Recommendation information output method, system and server

Examples

Experimental program
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Effect test

no. 1 example

[0031] see figure 1 , which is the flow chart of the first embodiment of the output method of recommendation information in this application:

[0032] Step 101: Extract user behavior data within a preset statistical period from the user behavior database.

[0033] The behavior data of all users’ operations on commodities will be saved in the user behavior database. The types of user behavior data include the number of clicks on commodities, the number of browsing times, the browsing time, the purchase amount, and the purchase quantity. Every time the interface completes a user action, the server saves the action data triggered by the user action into an entry corresponding to the user in the action database.

[0034] In the user behavior database, different user behavior data for the same product may be stored in different basic tables. When the user behavior data needs to be processed, the user behavior database can be searched according to the start time and end time of the...

no. 2 example

[0044] see figure 2 , which is the flow chart of the second embodiment of the output method of recommendation information in this application:

[0045] Step 201: Extract user behavior data within a preset statistical period from the user behavior database.

[0046] The behavior data of all users’ operations on commodities will be saved in the user behavior database. The types of user behavior data include the number of clicks on commodities, the number of browsing times, the browsing time, the purchase amount, and the purchase quantity. Every time the interface completes a user action, the server saves the action data triggered by the user action into an entry corresponding to the user in the action database.

[0047] In the embodiment of the present application, the processing of user behavior data can be started according to a trigger command input by the system, or can be automatically processed according to a preset time interval. In the user behavior database, differen...

no. 1 example

[0096] see Figure 4 , which is a block diagram of the first embodiment of the output server of the recommendation information of this application:

[0097] The server includes: an extraction unit 410 , a classification unit 420 , a calculation unit 430 , a receiving unit 440 and an output unit 450 .

[0098] Wherein, the extracting unit 410 is configured to extract user behavior data within a preset statistical period from the user behavior database;

[0099] A classification unit 420, configured to classify the user behavior data according to commodity identifiers, and obtain a time series of interest levels of each commodity within the statistical period;

[0100] A calculation unit 430, configured to calculate the peak purchase probability of the product according to the time series of the degree of interest;

[0101] a receiving unit 440, configured to receive an order for outputting recommendation information;

[0102] The output unit 450 is configured to sort the pur...

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Abstract

This application discloses a method, system and server for outputting recommendation information. The method includes: extracting user behavior data within a preset statistical period from a user behavior database; classifying user behavior data according to product identification, and obtaining each A time series of the degree of interest in a commodity within a statistical period; calculate the peak purchase probability of the commodity based on the time series of the degree of interest; when receiving a command to output recommendation information, the peak purchase probability is ranked from high to low. Sort and output product recommendation information based on the sorting results. This application automatically counts the purchase peak probability of goods in the time dimension based on user behavior data, which can improve the recommendation accuracy of the recommendation system and reduce the amount of unnecessary data transmission in the network; because the recommendation information is automatically corrected and output by the server , making full use of the computing power of the server, when adjusting the recommendation information by purchasing the peak probability, the amount of information input and computing resources of the recommendation system can be reduced.

Description

technical field [0001] The present application relates to the field of network technology, in particular to a method, system and server for outputting recommendation information. Background technique [0002] Online shopping has become a common way of shopping in people's lives. When users browse products and choose shopping on the website, the recommendation engine server of the website may recommend popular products to users, and present the relevant information of these popular products on the webpage. A specific location for the user to choose from. In the prior art, when outputting recommended product information, recommendations are only made according to the sales information of the product or the user's interest information (for example: clicks or page views). For example, if the historical purchase volume of a certain product exceeds a certain value , then recommend the product information to the user, or, if the number of clicks on a certain product exceeds a cert...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/00
CPCG06Q30/00G06Q30/0631G06F16/24578
Inventor 肖铨武苏宁军谭昶刘淇张金银陈恩红
Owner ALIBABA GRP HLDG LTD
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