Abstract
Community platforms featuring user sharing and self‐expression in social media generate big data on tourism resources, which, if fully utilized in a smart tourism system driven by high‐tech and new technologies, will bring new life to the field of smart tourism research and will play an important role in the development of Internet+ tourism. However, tourism data in social media has the following characteristics: diversity, redundancy, heterogeneity, and intelligence. To address the characteristics of tourism data in social media, this thesis focuses on the following challenges: it is difficult to efficiently obtain tourism visualization information (text and images) in social media; it is difficult to effectively utilize tourism multimodal heterogeneous information; it is difficult to properly retrieve multimedia entity information of tourism attractions; and it is difficult to reasonably construct tourism personalized recommendation models. In this paper, an image search reordering method based on a hybrid feature graph model is proposed to realize the rapid acquisition of high‐quality Internet images from the web using hybrid visual features and graph models, thus providing data security for the analysis of social media‐based tourism images. To address the shortcomings of current search engines for image retrieval, visual information is used to bridge the problem of semantic gap between text‐based search and images. To address the limitation of single visual features, we use latent semantic analysis to fuse multiple visual features to obtain hybrid features, which not only combine multiple single features but also preserve the potential relationship between these features. To address the shortcomings of the reordering methods based on classification and clustering, a reordering framework based on the graph model is used to reorder the images and finally complete the image search reordering based on the hybrid feature graph model. This method can obtain image information in social media with high efficiency and quality and then prepare for the subsequent work of tourism image analysis mining and personalized recommendation.
Highlights
Information technology has entered the fast lane of development, and the Internet has entered the Web 3.0 era, which means that the era of “Internet+integrated services” has come [1]
The basic idea of visual feature-based image search reordering is to use visual features to reorder the images returned by the search engine and return them to the user, hoping that the user can get the images related to the search query in the top images, and the ideal state of reordering is that the relevance of the top images is higher, saving the time of the user to find the images related to the query terms from the redundant results
Since the proposed reordering method combines BovW features and CM features for reordering and the comparison methods used are for single feature reordering, to ensure fairness, the BovW features and CM features are applied to the comparison method separately, and the Normalized Discounted Cumulated Gain (NDCG) values of the two features are averaged to obtain the mean value of NDCG to compare with the proposed method
Summary
Information technology has entered the fast lane of development, and the Internet has entered the Web 3.0 era, which means that the era of “Internet+integrated services” has come [1]. Data released by the National Tourism Administration in 2014 showed that for domestic travel, the number of tourists choosing travel agencies was only 3.6% of the total number of domestic trips; for foreign travel, the number of tourists choosing free travel was 65% of the total number of outbound trips [8] This phenomenon is due to the fact that people have access to a huge amount of travel information on the Internet, which facilitates travel planning and decision-making, which in turn has led to a great change in the way people travel and their perceptions in recent years. Without intelligently considering the comprehensiveness and preference of information, it often fails to recommend satisfactory results for users These shortcomings have severely restricted a series of travel behaviors such as access to high-quality travel information, demand-compliant destination recommendations, and personalized plan customization. The tourism industry, focusing on the current development needs, urgently needs to comply with the development of Internet+tourism informatization, actively mining tourism data and information, striving to explore intelligent and personalized tourism, and eventually making continuous efforts to realize intelligent tourism
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