Linus Dietz

Lecturer (Assistant Professor) in Computer Science Education

Designing a Conversational Travel Recommender System Based on Data-driven Destination Characterization

less than 1 minute read

Recommending complex, intangible items in a domain with high consequences, such as destinations for traveling, requires additional care when deriving and confronting the users with recommendations. To address these challenges, we developed a first version of the CityRec system, a destination recommender system that makes two contributions. The first is a data-driven approach to characterize cities according to the availability of venues and travel-related features, such as the climate and travel costs. The second is a conversational recommender system using unit critiquing with 180 destinations around the globe based on the data-driven characterization, which provides prospective travelers with inspiration for and information about their next trip. An online user study with 104 participants revealed that the proposed system has a significantly higher perceived accuracy compared to the baseline approach, however, at the cost of ease of use.

Linus W. Dietz, Saadi Myftija, and Wolfgang Wörndl. “Designing a Conversational Travel Recommender System Based on Data-driven Destination Characterization.” In: ACM RecSys Workshop on Recommenders in Tourism. Sept. 2019, pp. 17–21

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