Because of the transfer from brick-and-mortar stores to the web, tourism companies have had an increasing need for good recommendation systems to help the users of their websites find what they want. When developing a recommendation system for tourism, we run into a couple of problems that we would not run into when developing it for e-commerce. One of these problems is the increased effect of the cold start problem. This problem entails that we do not understand what new users are interested in because we have very little information about them. The increased effect of the problem is due to the low number of bookings that are made compared to e-commerce purchases. To reduce the effect of the cold start problem, we can use additional data sources in order to understand the user's interests better.

To simplify the use of the additional data source, we explore the possibility of embedding the data or using it in conjunction with an embedding. Vakanties.nl is a company with the need for an improved recommender system. Therefore, we decided to explore these possibilities in cooperation with Vakanties.nl. We develop a recommender system that is able to make recommendations, using both the embedding and clickstream data from Vakanties.nl. We find that although the results of our system do have potential, the system requires some further improvement to compete with a conventional recommendation system.