Interest recommendation method and system based on user sequence click behavior

An interest recommendation, user technology, applied in special data processing applications, instruments, electrical digital data processing and other directions, can solve the problem of not considering the internal structure of the sequence, only considering the item sequence pattern, ignoring the item sequence conversion relationship, etc. Conducive to parallel processing, easy parallel processing, and improved recommendation performance

Pending Publication Date: 2020-02-18
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The inventor found during the research and development process that the general sequence recommendation system takes the items that the user has interacted with as a whole as input. First of all, they do not take into account the internal structure of the sequence, that is, the sequence is composed of

Method used

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  • Interest recommendation method and system based on user sequence click behavior
  • Interest recommendation method and system based on user sequence click behavior
  • Interest recommendation method and system based on user sequence click behavior

Examples

Experimental program
Comparison scheme
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Example Embodiment

[0053] Example one

[0054] figure 1 It is a flowchart of an interest recommendation method based on a user's sequential click behavior involved in this embodiment. The interest recommendation method first divides the user's interaction sequence into different sessions according to the user's preferences in a certain period of time, and then combines the user interest in each session and the interest interaction between different sessions to implement sequence recommendation , To effectively make up for the existing sequence recommendation methods ignore the inherent structure of user sequence behavior and ignore the conversion relationship between items.

[0055] See attached figure 1 , The method of interest recommendation based on user sequence click behavior includes the following steps:

[0056] S101: Obtain a user's historical interactive item sequence.

[0057] Specifically, the user's historical interaction item data is acquired to form the user's historical interaction item ...

Example Embodiment

[0106] Example two

[0107] Figure 4 It is a structural diagram of an interest recommendation system based on a user's sequential click behavior involved in this embodiment. Such as Figure 4 As shown, the system includes:

[0108] The data acquisition module is used to acquire the user's historical interactive item data to form the user's historical interactive item sequence;

[0109] Model building module, used to build interest recommendation model;

[0110] The session division module is used to divide the user's historical interactive item sequence by using the interest recommendation model;

[0111] The in-session interest extraction module is used to extract each in-session interest obtained after division;

[0112] The activation module is used to assign different weights to the interest in each conversation to obtain the user's conversation interest sequence;

[0113] Inter-session interest interaction module, used to interact the interests between different sessions to obtain ...

Example Embodiment

[0115] Example three

[0116] This embodiment provides a computer-readable storage medium with a computer program stored on the computer-readable storage medium, and when the program is executed by a processor, the following steps are implemented:

[0117] Obtain the user's historical interactive item data to form the user's historical interactive item sequence;

[0118] Construct an interest recommendation model;

[0119] Use the interest recommendation model to divide the user's historical interactive item sequence into conversations;

[0120] Extract the interests in each session obtained after division, and perform weighting processing on the interests in each session to obtain the user's session interest sequence;

[0121] Interact the interests between different sessions to obtain a dynamic interaction model between different sessions;

[0122] Input the user's conversational interest sequence into the dynamic interaction model between different conversations, and predict the target...

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Abstract

The invention discloses an interest recommendation method and system based on a user sequence click behavior, and the method comprises the steps: firstly obtaining the historical interaction project data of a user, and forming a historical interaction project sequence of the user; constructing an interest recommendation model; carrying out session division on the historical interaction item sequence of the user by utilizing an interest recommendation model; extracting interests in each session obtained after division, and performing weighting processing on the interests in each session to obtain a session interest sequence of the user; interacting interests among different sessions to obtain a dynamic interaction model among different sessions; and inputting the session interest sequence of the user into a dynamic interaction model between different sessions, and predicting and obtaining a to-be-recommended target project sequence.

Description

technical field [0001] The invention relates to the technical field of item recommendation, in particular to an interest recommendation method and system based on user sequence click behavior. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] With the rapid development of the Internet industry, we have entered an era of information explosion. A wide variety of various projects, rapidly increasing news information, and overwhelming advertising information have seriously "overloaded" individuals' ability to accept. The huge amount of information on the Internet has brought huge challenges to both information providers and information users: how can information providers display the massive information they store to information users in a targeted manner; information users How to filter out the information you need from a lot of information....

Claims

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Application Information

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IPC IPC(8): G06F16/9536G06F16/9535G06N20/00
CPCG06F16/9536G06F16/9535G06N20/00
Inventor 刘方爱许明明鞠杰徐卫志
Owner SHANDONG NORMAL UNIV
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