Personalized multi-dimensional scenic spot recommendation method

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...

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 algorithm is too single, resulting in the recommended attractions not meeting the needs of users;

[0006] 3. The recommendation algorithm cannot dynamically change the recommendation model based on user feedback

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