Deep learning-based user interest point recommendation method and system

A deep learning and user interest technology, applied in neural learning methods, special data processing applications, instruments, etc., can solve problems such as low prediction accuracy, reduce search space, reduce search space, and improve user experience.

Active Publication Date: 2021-02-12
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above problems, the present invention proposes a user point of interest recommendation method and system based on deep learning, which can predict the POI that the user is interested in in a certain period of time in the future, and can overcome the accuracy of the prediction caused by the sparseness of check-in data. low degree problem

Method used

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  • Deep learning-based user interest point recommendation method and system
  • Deep learning-based user interest point recommendation method and system

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Experimental program
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Embodiment 1

[0031] In one or more implementations, a method for recommending user points of interest based on deep learning is disclosed, referring to figure 1 , including the following process:

[0032] (1) Obtain the user's historical check-in data;

[0033] (2) Train the deep learning model based on historical check-in data;

[0034] (3) Input the latest check-in data with predicted users into the trained deep learning model, and output the predicted user points of interest;

[0035] Wherein, the deep learning model automatically extracts user preference features for POI categories and POI preferences, and the two features are expressed as two feature Embeddings (Embedding refers to a vector containing certain potential information). Then, calculate the Euclidean distance between the two feature Embeddings and the candidate set POI Embedding, sort by score, and output the top N POIs; N is the set value.

[0036] Specifically, the historical check-in data of the user is acquired, spe...

Embodiment 2

[0079] In one or more implementations, a user point of interest recommendation system based on deep learning is disclosed, including:

[0080] The data acquisition module is used to acquire the user's historical check-in data;

[0081] The model training module is used to train the deep learning model based on historical check-in data. The Embedding mentioned in this application will be continuously adjusted during the deep learning training process;

[0082] The POI prediction module is used to input the latest check-in data of the predicted user into the trained deep learning model, and output the predicted point of interest of the user;

[0083] Wherein, the deep learning model automatically extracts user preference features for POI categories and POI preferences, and the two features are expressed as two feature Embeddings (Embeddings refer to vectors containing certain potential information). Then, calculate the Euclidean distance between the two feature Embeddings and t...

Embodiment 3

[0088] This embodiment also provides a terminal device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are executed by Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0089] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or ...

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Abstract

The invention discloses a deep learning-based user interest point recommendation method and system. The method comprises the steps of obtaining historical sign-in data of a user; training a deep learning model based on the historical sign-in data; inputting the latest sign-in data of the user into the trained deep learning model, and outputting predicted user interest points, wherein the deep learning model automatically extracts a POI category preference feature and a POI preference feature of a user, and the two features are expressed as two features Embedding; then, performing Euclidean distance calculation on the two kinds of characteristics Embedding and a candidate set POI Embedding, sorting through scores, and outputting POIs ranked as the top N, wherein N is a set value. Accordingto the invention, the situation that the recommendation accuracy can be influenced by the retrieval space with a huge POI corpus is considered, and the filtering module is linked after the preferenceencoder of the POI category, so that the POI retrieval space is reduced, the CatDM retrieval difficulty is reduced, and meanwhile, the recommendation accuracy is improved.

Description

technical field [0001] The present invention relates to the technical field of data analysis, in particular to a method and system for recommending user points of interest based on deep learning. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] In recent years, with the rapid development of location-based social network (Location-Based Social Network, referred to as LBSN), such as GoWalla, JiePang and Foursquare, the heat is increasing, recommending the next point of interest (Point-Of-Interests, referred to as POI) is also becoming more and more important. On such online platforms, users are allowed to check-in at points of interest (POIs) using their mobile devices. With the increase of information such as user behavior time, geography, friends, and tags collected by various applications, LBSN has accumulated a large amount of user chec...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06N3/04G06N3/08
CPCG06F16/9535G06N3/049G06N3/084G06N3/044Y02D10/00
Inventor 崔立真于福强郭伟何伟闫中敏鹿旭东
Owner SHANDONG UNIV
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