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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 sessions, and users interact with each other in the same session. Interests vary across sessions: user behavior is similar within each session, but differs across sessions
Second, they only consider the sequence patterns between items, but ignore the sequence transformation relationship between items

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
Effect test

Embodiment 1

[0054] figure 1 It is a flow chart of the interest recommendation method based on user sequence 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 preference within a certain period of time is similar, and then combines the user's interest in each session and the interest interaction between different sessions to achieve sequence recommendation. , which can effectively make up for the problems that the existing sequence recommendation methods ignore the internal structure of user sequence behavior and ignore the conversion relationship between items.

[0055] Please refer to the attached figure 1 , the method for recommending interests based on user sequence click behavior comprises the following steps:

[0056] S101. Acquire a user's historical interaction item sequence.

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

Embodiment 2

[0107] Figure 4 It is a structural diagram of the interest recommendation system based on user sequence 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] A model building block for building an interest recommendation model;

[0110] The session division module is used to use the interest recommendation model to perform session division on the user's historical interaction item sequence;

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

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

[0113] The inter-session interest interaction module is used to interact the interests between different ses...

Embodiment 3

[0116] This embodiment provides a computer-readable storage medium, on which a computer program is stored, 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] Build an interest recommendation model;

[0119] Use the interest recommendation model to segment the user's historical interaction item sequence into sessions;

[0120] Extract the interest in each session obtained after the division, and perform weight processing on the interest 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 session interest sequence into the dynamic interaction model between different sessions, and predict the target item sequence to be recommended.

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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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