Towards Generating Personalized Country Recommendation

The rise in international migration over the past decades has given more audience to this crucial issue of human life. According to reports by United Nations, more than 243 million people live in a country that is not their place of birth. People decide to immigrate, based on a range of reasons, and choose the country of destination with the hope to begin a new life. However, such a risky decision may not necessarily lead to an improvement of life and in many cases could result in complete dissatisfaction of the emigrating person, and in the extreme cases, cause human catastrophe. Recommender Systems (RSs) are tools that could mitigate this problem by supporting the people in their decision making process. RSs can interact with the people who are willing to immigrate and acquire certain information about their preferences on potential destinations. Accordingly, RSs can build predictive models based on the acquired data and offer suggestions on where could be a better match for the specific preferences and constraints of people. This work is an attempt to build a RS that can be used in order to receive personalized recommendation of countries. The system is capable of eliciting preferences of users in the form of ratings, learning from the preferences, and intelligently generating a personalized ranking list of countries for every target user. We have conducted a user study in order to evaluate the quality of the recommendation, measured in terms of accuracy, diversity, novelty, satisfaction, and capability to understand the particular preferences of different users. The results were promising and indicated the potentials of the generating personalized recommendations in this less-explored domain.