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Method and device for obtaining optimal parameter combination of recommendation system

A parameter combination and recommendation system technology, applied in the field of recommendation systems, can solve problems such as large quantities, insufficient confidence in user performance data, and inability to efficiently and accurately determine the optimal parameter combination of the recommendation system

Active Publication Date: 2021-01-05
BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The disadvantage of this method is that it is impossible to efficiently and accurately determine the optimal parameter combination of the recommendation system. The specific reason is: if the time interval for changing the value of the parameter combination is short, the user's performance data is not reliable enough to evaluate the system effect well; If the time interval for changing the value of the parameter combination is long, although the evaluation effect is more convincing, but because the parameter combination contains many parameters, the number of parameter combination values ​​increases exponentially with the number of parameters, and the number is huge, so it is difficult to evaluate in a limited number of parameters. Traverse all parameter combination values ​​in time

Method used

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  • Method and device for obtaining optimal parameter combination of recommendation system
  • Method and device for obtaining optimal parameter combination of recommendation system
  • Method and device for obtaining optimal parameter combination of recommendation system

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Experimental program
Comparison scheme
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Embodiment 1

[0059] see figure 1 , figure 1 It is a flow chart of the method for obtaining the optimal parameter combination of the recommendation system in Embodiment 1 of the present invention, including the following steps:

[0060] S110: Screen a plurality of experimental users.

[0061] According to actual needs, a certain proportion of users can be randomly sampled from the total number of users as experimental users. For example, randomly sample 1% of all users as experimental users. The sampling ratio can be set according to actual requirements. The higher the sampling ratio, the greater the processing capacity of this embodiment, but the better the effect of obtaining the optimal parameter combination.

[0062] S120: Perform a parameter test at the granularity of the experimental user's session (Session) to obtain a learning sample, where the learning sample includes partial values ​​of parameter combinations and their corresponding system effect evaluation values.

[0063] Di...

Embodiment 2

[0070] This embodiment introduces a specific implementation manner of step S120 in the first embodiment.

[0071] For ease of understanding, before introducing specific steps, two related concepts are introduced first.

[0072] First, the user's session:

[0073] In a recommendation system, user behavior patterns are usually refreshed and browsed in units of sessions. That is: starting from a certain moment, continuously refresh and browse and read articles for a period of time, then stop, and then continue to refresh and browse for a period of time after a period of time, and so on.

[0074] Let the user be u, and the i-th session of user u is recorded as s u (i). A user session can contain many refreshes, namely s u (i)={r u (i,j)|1u (i)}, where r u (i, j) is the j-th refresh in the i-th session of user u, n u (i) is the total refresh times in user u's ith session.

[0075] remember st u (i,j) is the starting time of the jth refresh in the ith session of user u, et ...

Embodiment 3

[0104] This embodiment introduces a specific implementation manner of step S140 in the first embodiment. see Figure 4 , Figure 4 It is the implementation flowchart of the third embodiment of the present invention.

[0105] In this embodiment, it is necessary to find the optimal system effect and its corresponding parameter combination value from the above system effect space, and the corresponding parameter combination value is the optimal parameter combination of the recommendation system. Since the amount of data contained in the system effect space is very large, it is impossible to find the optimal system effect through direct comparison. Therefore, this embodiment adopts the hill climbing method to obtain the optimal system effect in the system effect space. Refer to the following Figure 4 Details. Embodiment 3 of the present invention comprises the following steps:

[0106] S141: Randomly select the value P of multiple parameter combinations in the parameter comb...

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Abstract

The invention provides a method, an apparatus, a device and a storage medium for obtaining an optimal parameter combination of a recommendation system, wherein the method comprises the following stepsof: screening a plurality of experiment users; Taking the session of the experimental user as the granularity to carry out the parameter experiment, and obtaining a learning sample, wherein the learning sample comprises a part of the value of the parameter combination and a corresponding system effect evaluation value; training The machine learning model by using the learning samples, and acquiring the mapping relationship between the parameter combination space and the system effect space, wherein the parameter combination space comprises all the values of the parameter combination; acquiring The optimal system effect in the system effect space and the corresponding parameter combination value by using the mapping relationship, and the obtained parameter combination value is taken as theoptimal parameter combination of the recommendation system. The embodiment of the invention can efficiently and accurately obtain the optimal parameter combination of the recommendation system.

Description

technical field [0001] The present invention relates to the technical field of recommendation systems, in particular to a method, device, device and computer-readable storage medium for obtaining an optimal parameter combination of a recommendation system. Background technique [0002] The emergence and popularization of the Internet has brought a large amount of information to users, which meets the needs of users for information in the information age. However, due to the large amount of information, users cannot get what is really useful to them when faced with a large amount of information. For some information, the efficiency of using information is reduced instead, which is the so-called information overload problem. [0003] One way to solve the problem of information overload is to use a recommendation system, which is a personalized information recommendation system that recommends information, products, etc. that users are interested in to users according to their ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06N3/00
CPCG06N3/006
Inventor 刘峰金慈航
Owner BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD