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A Personalized Travel Travel Recommendation Method Based on Probabilistic Graph Model

A probabilistic graph model and recommendation method technology, applied in the direction of instruments, data processing applications, data mining, etc., can solve the problems of sparse data, not involving tourist locations, uneven data distribution, etc., and achieve the effect of improving accuracy

Active Publication Date: 2020-11-06
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In terms of text content processing, such as text classification, the common method is a probabilistic model (such as Naive Bayesian, LDA), but these models may encounter data sparseness, uneven data distribution, etc., and do not involve tourist locations. Related Information

Method used

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  • A Personalized Travel Travel Recommendation Method Based on Probabilistic Graph Model
  • A Personalized Travel Travel Recommendation Method Based on Probabilistic Graph Model
  • A Personalized Travel Travel Recommendation Method Based on Probabilistic Graph Model

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Experimental program
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Embodiment 1

[0059] Such as figure 1 As shown, a personalized travel recommendation method based on a probabilistic graphical model includes the following steps:

[0060]S1: Travel note topic initialization: Segment the travel note articles, adopt the standard article topic model, and obtain the topic distribution of each travel note and the topic distribution of each word through Gibbs sampling, and use the calculated topic distribution to compare travel notes and The relevant parameters of word gamma distribution are assigned. In addition, the relevant parameters of user preferences and location hidden features are assigned initial values ​​with random numbers;

[0061] S2: For each word in each travel note, calculate the logarithmic value of the word frequency relationship through the distribution of word topics and article topics, and update each travel note and the shape parameter in the gamma distribution parameters of the words in the travel note;

[0062] S3: For each travel note ...

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Abstract

The invention provides a probabilistic graphical model-based personalized travel note recommendation method. According to the method, estimation is carried out on unknown user preferences and site features by adoption of gamma distribution and a Poisson decomposition algorithm, the hidden features can be mined by utilizing three pieces of information such as text information, sites and whether the travel nots are commented or not, and messages which cannot be obtained, such as geographic positions of readers and positions of scenic spots do not need to be considered, so that the recommendation correctness can be improved; and by adoption of a joint probabilistic graphical model, the common cold start problem in recommendation systems and the travel nots with more graphics and few words can be well d.

Description

technical field [0001] The invention relates to the field of text recommendation algorithms in data mining recommendation algorithms, and more specifically, to a method for recommending personalized travel notes based on a probability graph model. Background technique [0002] With the development of society and the improvement of people's living standards, more and more people have time and money to travel, and even go abroad to travel abroad. At the same time, with the development of the Internet, Internet social platforms related to tourism are also developing greatly. On these platforms, tourist users record their travel experiences with words, and record the details of travel with photos. People watch their favorite travelogues and comment on them. In the relatively large travel websites in China, such as Baidu and Mafengwo, according to statistics, 1 / 10 people have the habit of writing their own travel notes, while the rest of the users did not leave their own travel ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06F40/284G06F40/258G06F40/268G06F16/14G06F16/16G06Q50/14
CPCG06F16/148G06F16/164G06F16/9535G06F40/258G06F40/268G06F40/284G06F2216/03G06Q50/14
Inventor 安孝杰任江涛
Owner SUN YAT SEN UNIV