Integrating Sentiment Analysis and User Descriptors with Ratings in Sightseer Recommender System

Since last two decades there is a rapid growth in the globalized world for recommender systems. These provide users rich insights in diverse applications such as healthcare, e-commerce, education, and tourism etc. Hence there is a growing demand to accurately analyze the reviews posted by the users on different social media sites. Tourism industry's economy largely relies on analysis of the above mentioned data. Hence we have pursued the idea of building a recommender system which will provide users with valuable insights and help them in making the correct choice. In our approach we have experimented on the data collected for 150 locations from and near Pune city, in Maharashtra representing the country India. We first categorized the reviews into location specific details. The defined categories are 'Temple', 'Historical', 'Hill Station' and 'Educational'. We then integrated the ratings provided by the previous users under each category with those of user's interests like Expense, total number of days, distance for trip and the user's interests. The combined approach will be capable of recommending a set of tours that most closely matches with the user's interests and thus enabling them to make the best choice.