Travel scheme recommendation algorithm based on tourist portraits and LBS data

Abstract

The invention discloses a travel scheme recommendation algorithm based on tourist portraits and LBS data. The algorithm sequentially comprises the following steps of tourist feature modeling, touristfeature extraction based on big data analysis, similar tourist group modeling, similarity calculation between tourists and similar tourist groups and message pushing using similarity. Modeling by tourist features, big data analysis is used to extract tourist level features; under the condition that the tourists do not generate more behaviors, the behavior preferences of the tourists are replaced by the behavior preferences of the similar tourist groups, and then the messages which cannot be matched with the tourists originally are pushed to the tourists through the matching of the similar tourist groups, so that the mode of pushing the tourist information in an inundation mode without destinations is effectively solved.

Description

technical field [0001] The invention belongs to the technical field of data analysis, and relates to a travel plan recommendation algorithm based on tourist portraits and LBS data. Background technique [0002] The functions of mobile phones are becoming more and more powerful. Tourists use mobile phones to browse relevant information of tourist attractions, inquire about other tourists' travel experience and strategy, or rely on the information pushed by merchants to travel, which has become the main way of tourism at present. [0003] However, this way of traveling has some shortcomings and is not humanized enough. For example, it is time-consuming and labor-intensive for users to actively browse the relevant information of tourist attractions; it is difficult for tourists to directly apply the travel experience and strategy of other tourists due to their different itineraries and preferences; the information pushed Lack of status and location association, even if tourists...

AI Technical Summary

Problems solved by technology

[0003] However, this way of traveling has some shortcomings and is not humanized enough. For example, it is time-consuming and labor-intensive for users to actively browse the relevant information of tourist attractions; it is difficult for tourists to directly apply the travel experience and strategy of other tourists due to their different itineraries and preferences; the information pushed Lack of status and location association, even if tourists are interested, they may give up the target itinerary due to the distance and unfamiliar routes; marketing information is mainly pushed to users in a unified manner, which is not well targeted and has been aimless for a long time Pushing marketing information will cause browsing and consumption fatigue to weakly targeted users, and even cause the loss of users

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