Personalized multi-dimensional scenic spot recommendation method

A recommendation method and multi-dimensional technology, applied in the field of tourism industry, can solve problems such as the single content considered by the recommendation algorithm, the recommended attractions do not meet the needs of users, and the lack of authenticity

Active Publication Date: 2020-08-04
XIANGTAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] 1. Some only recommend popular scenic spots without fully considering the individual needs of tourists, resulting in a lack of authenticity;
[0005] 2. The content considered by the recommendation alg

Method used

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  • Personalized multi-dimensional scenic spot recommendation method
  • Personalized multi-dimensional scenic spot recommendation method
  • Personalized multi-dimensional scenic spot recommendation method

Examples

Experimental program
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Embodiment Construction

[0157] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0158] Such as figure 1 As shown, a personalized multi-dimensional attraction recommendation method includes the following steps:

[0159] 1) Preliminarily stipulate the scope of recommended scenic spots and obtain data.

[0160] Such as figure 2As shown, the way to recommend the range of scenic spots is: to obtain the maximum value D of the distance between the user's current location and all scenic spots max , taking the user's current location as the center of the circle, take 1 / 5D max Set a circular range for the radius, and include all scenic spots within the range in the initially screened scenic spot range; if the number of scenic spots in the filtered range is less than 1 / 5 of the total number, go to the scenic spot according to the user's current location According to the distance of the location, the remaining scenic spots will be added t...

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Abstract

The invention discloses a personalized multi-dimensional scenic spot recommendation method. The method comprises the following steps of: stipulating a recommended scenic spot range and acquiring data;constructing an interest scoring model based on an interest drift algorithm; constructing a scene scoring model; constructing a scenic spot pedestrian volume model; constructing a scenic spot comfortscoring model; constructing a scoring model based on the distance; integrating the interest scoring model, the scenery scoring model and the scenic spot comfort scoring model, and constructing a multi-dimensional scenic spot scoring model based on the scoring model of the distance; and constructing a feedback correction model, correcting the multi-dimensional scenic spot scoring model, and recommending scenic spots according to the corrected multi-dimensional scenic spot scoring model. The scenic spots are scored by comprehensively considering four factors including the environment, the pedestrian volume (comfort level), the scenery and the distance, the scenic spots are intelligently recommended to the user according to personalized requirements of the user, and the experience degree ofthe user is improved.

Description

technical field [0001] The invention relates to the field of tourism industry, in particular to a personalized multi-dimensional scenic spot recommendation method. Background technique [0002] The number of tourists and tourism income in our country continue to grow rapidly, and the tourism industry is developing rapidly. Tourism has become an important part of the tertiary industry. In order to achieve a higher level of promotion and sustainable development of the tourism industry, personalized and intelligent self-help tourism is the future development trend of the tourism market. [0003] For tourists, in the case of limited travel time and insufficient understanding of the scenic spots, it is difficult to choose the one that suits them from the many tourist attractions in the scenic spot. Therefore, there have been many practical scenic spot recommendation systems on the market. However, most of these systems are not highly personalized, and their main problems are re...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06K9/00G06F17/18G06Q50/14
CPCG06F16/9535G06F17/18G06Q50/14G06V40/10G06V20/53
Inventor 刘炜房海鹏蒋萌熊颖童刘慧琳甘文清
Owner XIANGTAN UNIV
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