Linus W. Dietz

About Linus W. Dietz

Linus W. Dietz, With an exceptional h-index of 9 and a recent h-index of 9 (since 2020), a distinguished researcher at Technische Universität München, specializes in the field of Urban Computing, Recommender Systems, Data Science, Software Engineering.

His recent articles reflect a diverse array of research interests and contributions to the field:

Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms

Data-driven Destination Recommender Systems

Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation

A Comparative Study of Data-driven Models for Travel Destination Characterization

Recommending the Duration of Stay in Personalized Travel Recommender Systems

Navigation by revealing trade-offs for content-based recommendations

An Interactive Dashboard for Traveler Mobility Analysis.

TripRec–A Recommender System for Planning Composite City Trips Based on Travel Mobility Analysis

Linus W. Dietz Information

University

Position

Researcher at

Citations(all)

199

Citations(since 2020)

188

Cited By

66

hIndex(all)

9

hIndex(since 2020)

9

i10Index(all)

9

i10Index(since 2020)

9

Email

University Profile Page

Google Scholar

Linus W. Dietz Skills & Research Interests

Urban Computing

Recommender Systems

Data Science

Software Engineering

Top articles of Linus W. Dietz

Title

Journal

Author(s)

Publication Date

Understanding the Influence of Data Characteristics on the Performance of Point-of-Interest Recommendation Algorithms

arXiv preprint arXiv:2311.07229

Linus W Dietz

Pablo Sánchez

Alejandro Bellogín

2023/11/13

Data-driven Destination Recommender Systems

Linus W. Dietz

2023/4/10

Travelers vs. Locals: The Effect of Cluster Analysis in Point-of-Interest Recommendation

Pablo Sanchez

Linus W Dietz

2022/7/4

A Comparative Study of Data-driven Models for Travel Destination Characterization

Frontiers in big data

Linus W Dietz

Mete Sertkan

Saadi Myftija

Sameera Thimbiri Palage

Julia Neidhardt

...

2022/4/7

Recommending the Duration of Stay in Personalized Travel Recommender Systems

Abhishek Agarwal

Linus W Dietz

2022/9/22

Navigation by revealing trade-offs for content-based recommendations

Linus W Dietz

Sameera Thimbiri Palage

Wolfgang Wörndl

2022

An Interactive Dashboard for Traveler Mobility Analysis.

Lukas Vorwerk

Linus W Dietz

2021

TripRec–A Recommender System for Planning Composite City Trips Based on Travel Mobility Analysis

Rinita Roy

Linus W Dietz

2021

Analyzing ‘Near Me’Services: Potential for Exposure Bias in Location-based Retrieval

Ashmi Banerjee

Gourab K Patro

Linus W Dietz

Abhijnan Chakraborty

2020/12/10

Mining trips from location-based social networks for clustering travelers and destinations

Information Technology & Tourism

Linus W Dietz

Avradip Sen

Rinita Roy

Wolfgang Wörndl

2020/3

CityRec--A Data-Driven Conversational Destination Recommender System.

E-review of Tourism Research

Saadi Myftija

Linus W Dietz

2020/1/15

See List of Professors in Linus W. Dietz University(Technische Universität München)

Co-Authors

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