Most apps that have a running route recommendation feature suggest routes based on distance, surface, elevation, and popularity, disregarding other factors that influence the psychological experience of running
Point-of-interest (POI) recommendations are essential for travelers and the e-tourism business. They assist in decision-making regarding what venues to visit and where to dine and stay. While it is known that traditional recommendation algorithms’ performance depends on data characteristics like sparsity, popularity bias, and preference distributions, the impact of these data characteristics has not been systematically studied in the POI recommendation domain. To fill this gap, we extend a pr...
As we witness an ongoing centralization of the population towards urban landscapes, recommendation technology has an increasing role in people’s perception and decision-making in cities. People use recommender systems for choosing places to stay, which attractions and restaurants to visit, and route recommender systems for navigation. This workshop aims to bring together practitioners and academics working on improving recommendations in the urban space with an emphasis on both individual nee...
This thesis explores various aspects of the destination recommendation domain, namely mobility analyses about which types of travelers can be observed in location-based social media data, how these groups perform in point-of-interest recommendation, how long travelers should stay at a destination, and which data sources are suitable to characterize destinations. Finally, we propose a city recommender system that supports users making the trade-offs involved in their travel choices.
The involvement of geographic information differentiates point-of-interest recommendation from traditional product recommendation. This geographic influence is usually manifested in the effect of users tending toward visiting nearby locations, but further mobility patterns can be used to model different groups of users.
To evaluate which data sources are most suitable to characterize destinations, we investigate 18 characterization methods that fall into the following categories: venue data, textual data, and factual data.
Tourism is a complex domain for recommender systems because of the high cost of recommending an unsuitable item and the absence of ratings to learn user preferences.
This is the first major publication of my dissertation project, which extends and combines previous work on the analysis of traveler mobility.
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.
Recommender systems could benefit from not only recommending the most fitting items but also in what quantity the user should consume them.
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Together with Wolfgang Wörndl and Daniel Herzog I will be teaching a seminar on ‘Current Topics in Recommender Systems’ next semester.
I am very happy to announce the release of ‘Java by Comparison’ by Simon Harrer, Jörg Lenhard and myself published at the Pragmatic Bookshelf.
After a technical downtime of shutting down my old server last December, I have finally set up a new homepage again.