Abstract
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. In order to address these challenges, we developed CityRec, a destination recommender 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 costs of travel. The second is a conversational recommender system 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.
| Original language | English |
|---|---|
| Pages (from-to) | 18-20 |
| Number of pages | 3 |
| Journal | CEUR Workshop Proceedings |
| Volume | 2435 |
| State | Published - 2019 |
| Event | 2019 Workshop on Recommenders in Tourism, RecTour 2019 - Copenhagen, Denmark Duration: 19 Sep 2019 → … |
Keywords
- Cluster analysis
- Conversational recommender systems
- Data mining
- Tourism recommendation