Ecotourism Recommendations based on Sentiments Using Skyline Query and Apache-Spark

Abstract

The selection of an ecotourism destination is a challenging service in an online transaction. The process must consider personal considerations, such as costs or distance and interesting eco-points like specific sceneries or the rare and unique picturesque landscapes. Only a few tourists have such required information for any particular local resources. A proposed recommender system is a solution for tourists to get advice on appropriate ecotourism destinations based on sentiments according to their preferences. This work proposed the skyline query method based on the Skyline Sort Filter algorithm in the Apache Spark cluster computing framework to build recommendations. The sentiment analysis process using the SentiStrength algorithm obtain an accuracy of 78.3% and F-arithmetic of 84.5%. These results indicate the proposed recommender system can detect positive responses from visitors to ensure best ecotourism recommendations with positive sentiments for tourist. Apache Spark with three computer nodes has 213.7 times faster execution time on correlated data, 240 times faster on independent data, and 288.1 times faster on anti-correlated data than a single computing method.