Travelogue

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As a tourist, one faces many difficulties from the moment he lands. Finding the right tourist guide, getting them at the right price and getting rid of the same old monotonous itinerary; These are some of the problems which motivated us to build Travelogue.

Travelogue's greatest strength is that it creates a personalized user experience in every event or interaction that the user has or is related to the user's interest in some way or the other. It successfully matches customers to signed up local guides using it's advanced matching allgorithm.

The project is based with its front end developed in HTML, CSS, JavaScript, PHP, jQuery and Bootstrap. This is supported by a strong back end Flask framework (Python). We used the basic SQLite3 database for instant prototype deployment of the idea and used Java and Faker library to create massive amounts of training data.

Throughout the project, we ran into designing issues such as frame layouts and how to display the data in the most effective manner, we believe in this constant update of design which would result in the creation of the best design suited for our web application. We also faced some difficulty while integrating the back end of the application written in Python with the front end created using JavaScript and PHP.

This hack was a solution to the common existing problem of matching people with similar interests. We brainstormed a lot and came up with a 3 step algorithm to tackle the issue. The algorithm focuses on the importance of features and how a customer relates to the guide using those features.

Working with a team of diverse skill sets, all of us learned a lot from each other and by overcoming the challenges we faced during the development of this web application.

We have planned to incorporate Facebook's massive data and widely popular Graph API to extract features and interests for matching customers with the local guides. After implementing these ideas, the user has a simple one click Facebook login which helps our matching algorithm work on large amount of meaningful data.