Location Aware Content Priority based Recommendation System Flying Squirrel Optimization - Deep Alternative Neural Network (FSO-DANN)
Keywords:
Recommendation System, Location Aware Content, Neural Network, Optimization, Priority based System, Performance MeasuresAbstract
Social networks collect a lot of customer information, and this data may be utilized to develop knowledge for a variety of mobile and web applications. The Recommendation System (RS) is a domain garnering much attention these days. There are numerous itinerary and location aware content in RS available right now, some of that are nearly exclusively business. However, a thorough analysis demonstrates the need for study and advancement in this field. The data supporting this study show that the majority of systems are essentially destination RSs, and the great majority do not dynamically build routes but instead require the customer to choose appropriate locations. Some need greater user involvement while others fail to account for the length of presence at the selected sites. In certain frameworks, the community finding technique was ineffective, while in others, the routes are not the best. The RS was developed to fill the holes in the existing itinerary RS that were discovered through a thorough analysis. A Flying Squirrel Optimization - Deep Alternative Neural Network (FSO-DANN) based location RS was in charge of giving customers to get the priority of the best location. A backtracking-based approach is implemented in the genetic algorithm-based itinerary planning component to create itineraries. The Hadoop Map Reduce programming method was used to build the system in parallel. An extensive investigation of the system's assessment reveals that it is effective and competent enough to offer a trip schedule to a user that was better suitable in 25% less time than existing systems.
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