Daniel Herzog

Technical University of Munich

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Wolfgang Wörndl

Technical University of Munich

Linus W. Dietz

Technical University of Munich

Christopher Laß

Technical University of Munich

Sajjad Ali Siddiqui

Technical University of Munich

Herbert L. Ray

Peter MacCallum Cancer Centre

Carlos Frederico

Nava Tintarev

Maastricht University

Cihan KÜDEN

Cukurova University

Hüseyin Nezih Özdemir

Ege University

Richard Horwat

University of Pittsburgh

Cooperative Institutions

American Association of Endodontists

Universidade de São Paulo

Technical University of Munich

Division of Undergraduate Education

Niigata University

Hacettepe University

Universidade Federal do Rio Grande do Sul

Clinical Research Management

Matsumoto Dental University

Cukurova University

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You Do Not Decide for Me! Evaluating Explainable Group Aggregation Strategies for Tourism

Shabnam Najafian Daniel Herzog Sihang Qiu Oana Inel Nava Tintarev

Most recommender systems propose items to individual users. However, in domains such as tourism, people often consume items in groups rather than individually. Different individual preferences in such a group can be difficult to resolve, and often compromises need to be made. Social choice strategies can be used to aggregate the preferences of individuals. We evaluated two explainable modified preference aggregation strategies in a between-subject study (n=200), and compared them with two baseline strategies for groups that are also explainable, in two scenarios: high divergence (group members with different travel preferences) and low divergence (group members with similar travel preferences). Generally, all investigated aggregation strategies performed well in terms of perceived individual and group satisfaction and perceived fairness. The results also indicate that participants were sensitive to a dictator-based strategy, which affected both their individual and group satisfaction negatively (compared to the other strategies).

Aggregation problem

Baseline (sea)

Divergence (linguistics)

10.1145/3372923.3404800

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Citations (4)

Deriving Tourist Mobility Patterns from Check-in Data

Web Search and Data Mining (2018)

Linus W. Dietz Daniel Herzog Wolfgang Wörndl

Recommendations in complex scenarios require additional knowledge of the domain. Planning a composite travel spanning several countries is a challenging, but encouraging domain for recommender systems, since users are in dire need for assistance: Information in typical publications, such as printed travel guides or personal blogs is often imprecise, biased or outdated. In this paper we motivate a data-mining approach to improve destination recommender systems with learned travel patterns. Specifically, we propose a methodology to mine trips from location-based social networks to improve recommendations for the duration of stay at a destination. For this we propose a model for combining data from different sources and identify several metrics that are useful to ensure sufficient data quality, i.e., whether a traveler's check-in behavior is adequate to derive patterns from it. We demonstrate the utility of our approach using a Foursquare data set from which we extract 23,418 trips in 77 countries. Analyzing these trips, we determine the travel durations per country, how many countries are typically visited in a given time span and which countries are often visited together in a composite trip. Also, we discuss how this method can be generalized to other recommender systems domains.

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A Multi-Tier Web Service and Mobile Client for City Trip Recommendations

Christopher Laß Wolfgang Wörndl Daniel Herzog

This demo paper outlines a system architecture for mobile city trip planning. We present a mobile application for recommending a route comprising multiple points of interests between a starting and a destination location based on personal preferences. We evaluated the app in a preliminary user study

Mobile apps

10.4108/eai.30-11-2016.2267194

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Citations (10)

Real-Time Public Transport Navigation on Smartwatches

International Conference on User Modeling, Adaptation, and Personalization (2017)

Sajjad Ali Siddiqui Daniel Herzog Wolfgang Wörndl

Smartwatch

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Recommending a sequence of interesting places for tourist trips

Information Technology & Tourism (2017)

Wolfgang Wörndl Alexander Hefele Daniel Herzog

Point of interest

10.1007/s40558-017-0076-5

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Citations (55)

A User Interface Concept for Context-Aware Recommender Systems

Patrick Hiesel Wolfgang Wörndl Matthias Braunhofer Daniel Herzog

Interface (matter)

10.18420/muc2016-mci-0092

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Citations (4)

Integrating Route Attractiveness Attributes into Tourist Trip Recommendations

Daniel Herzog Sherjeel Sikander Wolfgang Wörndl

Tourist trip recommender systems (RSs) support travelers in identifying the most attractive points of interests (POIss) and combine the POIss along a route for single- or multi-day trips. Most RSs consider only the quality of POIss when searching for the best recommendation. In this work, we introduce a novel approach that also considers the attractiveness of the routes between POIss. For this purpose, we identify a list of important attributes of route attractiveness and explain how to implement our approach using three exemplary attributes. We develop a web application for demonstration purposes and apply it in a small preliminary user study with 16 participants. The results show that the integration of route attractiveness attributes makes most people choose the more attractive route over the shortest path between two POIss. This paper highlights how tourist trip RSs can support smart tourism. Our work aims to encourage further discussion on collecting and providing environmental data in cities to enable such applications.

RSS

10.1145/3308560.3317052

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Citations (13)

12 Tourist trip recommendations – foundations, state of the art and challenges

De Gruyter eBooks (2023)

Daniel Herzog Linus W. Dietz Wolfgang Wörndl

Tourist Trip Design Problems (TTDPs) deal with the task to support tourists in creating a trip composed of a set or sequence of points of interests (POIs) or other items related to travel. This is a challenging problem for personalized recommender systems (RSs), because it is not only needed to discover interesting POIs matching the preferences and interests, but also to combine these destinations to a practical route. In this chapter, we present the TTDP and show how it can be modeled using different mathematical problems. We present trip RSs with a focus on recommendation techniques, data analysis and user interfaces. Finally, we summarize important current and future challenges that research in the field of tourist trip recommendations faces today. The chapter concludes with a short summary.

RSS

Field trip

Point of interest

Sequence (biology)

10.1515/9783110988567-012

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Citations (1)

Exploiting Item Dependencies to Improve Tourist Trip Recommendations.

Conference on Recommender Systems (2016)

Daniel Herzog Wolfgang Wörndl

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Citations (4)

Spontaneous Event Recommendations on the Go

Daniel Herzog Wolfgang Wörndl

In this paper, we summarize our previous work in the field of event recommendations and give an outlook on future work. We developed a recommender system which implements a hybrid recommendation technique to provide accurate recommendations. A two-week user study showed that our system delivers promising results. Nevertheless, we believe that this approach mainly supports users who are looking for recommendations a long time in advance. Other possible situations in which people could be open for recommendations are more spontaneous, for example, when they are already out exploring the city. We give an overview of aspects which have to be considered fur such spontaneous recommendations on the go and the role of social context in this scenario.

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