Tourist Attraction Recommendation Method and Data Management Based on Big Data Analysis

Abstract

The proliferation of social networking data has opened up numerous avenues for providing additional perspectives to decision-makers. While big data analytics has the potential to aid in rational decision, so far there is little evidence to support this claim. More importantly, in the tourism industry specifically, a standardized approach to assessing social video big data for strategic planning has not yet been created. This project will use a research-based design science method to develop and estimate a “big data analytics” strategy for enhancing strategic decision-making in the control of tourist destinations. Using geotagged pictures provided by visitors to the picture social-media site Flickr as a matter of reality, with Melbourne, Australia as a case study, this method’s applicability is demonstrated in helping destination management organizations analyze and predict tourist behavioral patterns at specific destinations. Extra source, recipient, and stakeholder groups were used to verify relevance. The produced artifact exemplifies a technique for assessing massive amounts of unstructured data to aid strategic decision-making in an actual problem area. The scope of the suggested method is examined, and it is possible that it could be applied to other types of extremely large data sets.