Multi-Ontology Based Points of Interests (MO-POIS) and Parallel Fuzzy Clustering (PFC) Algorithm for Travel Sequence Recommendation with Mobile Communication on Big Social Media

Leveraging community-contributed information such as blogs, Global Positioning System, and images that are geographically tagged for suggestions on navigation with mobile based communication is becoming a research area. The travel recommender a system is based on Points Of Interests (POIs) of user’s emerge in many studies and enterprise applications. Most of the preceeding travel recommendation methodologies are capable of recommending a personalized travel by considering the POIs. The major objective of this paper is to design a new Multi-Ontology based Points of Interest (MO-POIs) travel recommendation system which considers the POIs of the user along with the individual semantic information. Initially stage, the personalized MO-POIs sequence is recommended, and the routes are ranked based on the individual depending on the package he uses. Both the user and route package similarities are evaluated using parallel fuzzy clustering algorithm in this research work. Map Reduce is used as a parallel computation model in this research which performs computation with the help of a couple of functions that are termed as map and reduce. The social users travel records are used to optimize the top ranked routes. Finally, the new MO-POIs recommendation system evaluation is finished with a billion of Flicker images, uploaded by thousands of users and also more than 24,000 travelogues that cover the estimated 850 and above travel POIs in nine renowned cities, and illustrates its efficiency. The results are measured using the performance metrics like average precision and weighted average precision. The evaluation concludes that the proposed algorithm is capable of performing well in recent works that uses travel logs.