Research on Night Tourism Recommendation Based on Intelligent Image Processing Technology

Abstract

The rapid development of the tourism industry and the Internet era has led to an increasingly severe problem of travel information overload, and travel recommendation methods are essential to solving the information overload problem. Traditional recommendation algorithms only target common travel scenarios during the daytime, combining the ratings and necessary attributes between users and items to calculate similarity for a recommendation. Still, the research on night travel recommendations is one of the few scenarios that needs to be explored urgently. This paper, based on histogram equalization, achieves better experimental results on image enhancement, combines convolutional neural network technology with night image and text comment feature extraction technology, and evaluates the resulting error with mean absolute error (MAE). This paper presents the first night travel recommendation system. It compares it with the traditional collaborative filtering method, and the model proposed in this paper can effectively reduce the prediction error.

Intelligent recommendation method of intelligent tourism scenic spot route based on collaborative filtering

“…Many researchers argue that personalized recommendations should be a top priority when designing travel recommendation models. For example, some researchers propose creating personalized itineraries for visitors based on information they provide about themselves and their interests on the Minube website, as well as popular attractions in the region [32]. To make more accurate and tailored recommendations, some researchers analyze users' Flickr photos to determine their historical itineraries and preferences.…”