Abstract
Like in many other branches of modern economies, the internet changed the behavior of suppliers and customers in real estate markets. Recommender systems became more and more important in providing customers with a fast and easy way to find suitable real estate items.
In this paper, we show different possibilities for embedding the recommendation engine into the user journey of a real estate portal. Moreover, we present how additional information regarding real estate items can be incorporated into the recommendation process. Finally, we compare the recommendation quality of the state-of-the-art approaches deep learning and factorization machines with collaborative filtering (the currently used recommender algorithm) based on a data set extracted from the productive system of the Immowelt Group.
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Knoll, J., Groß, R., Schwanke, A., Rinn, B., Schreyer, M. (2018). Applying Recommender Approaches to the Real Estate e-Commerce Market. In: Hodoň, M., Eichler, G., Erfurth, C., Fahrnberger, G. (eds) Innovations for Community Services. I4CS 2018. Communications in Computer and Information Science, vol 863. Springer, Cham. https://doi.org/10.1007/978-3-319-93408-2_9
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