Data-Driven Destination Recommender Systems

Given vast number of possible global travel destinations, choosing a destination has become challenging. We argue that traditional media are insufficient to make informed travel decisions, due to a lack of objectivity, a lack of comparability and because information becomes out of date quickly. Thus, travel planning is an interesting field for data-driven recommender systems that support users to master information explosion. We present unresolved research questions with working packages for a doctoral project that combines the fields of recommender systems and user modeling with data mining. The core contributions will be a framework that integrates heterogeneous data sources from the travel domain, novel user modeling techniques and constraint-based recommender algorithms to master the complexities of global travel planning.