Collective Intelligence Services


Customers interact in online-communities to help each other sharing their knowledge. Thereby they advise each other or develop solutions commonly interacting in groups.

Goal is the creation of new individualized customer-services. Therefore, the interactive knowledge exchange is examined and the knowledge of the individuals as well as the knowledge of the group is extracted and analyzed by data mining algorithms. With the aid of this new generated knowledge, customer needs are detected and new knowledge about products and services is developed. Afterwards new services are conceptualized, fitting the individual customer needs.

Research Picture


Project description

In the context of this research topic an online-travel platform is designed. The platform enables users to input their former trips, recommend travels to each other and search for interesting trips. Additionally the users are motivated by playful elements to input their travel data. Customers use the travel community to share their own knowledge and experiences. Moreover they learn from the experiences of other users before planning their trips. Caused by this interactive knowledge exchange new collective knowledge about travel destinations arises. By talking about insider tips, customers share new knowledge, travel providers haven´t known before. Furthermore they state their own travel preferences.

Actual studies

Measuring customer needs using images:

Goal is to determine the relation between images users load up in online-communities and their characteristics.

  • Is it possible to conclude user characteristics from the pictures they like? It is determined if users who choose similar pictures also have similar characteristics (e.g. being sociable) or travel behavior.
  • Another step is to recognize relations between pictures users load up in online communities and their characteristics and travel behavior automatically. Therefor profile-pictures, travel-pictures as well as data about former travel behavior and characteristics are collected in several experiments. Afterwards, different data and image mining algorithms are used for pattern recognition.

Automatically generated individual travel recommendation

Another study deals with automatically generated travel recommendations. Therefore several input data and algorithms are used in order to optimize the recommendations. A special focus is the involvement of human recommendation to optimize the machine generated recommendations.

Related Student Reports (excerpt)

  • Konzeption und Implementierung interaktiver Elemente zur personalisierten Konfiguration und Visualisierung von Reiseempfehlungen
    (Isabella Eigner; Master Thesis; 2014)
  • Using travel photos to generate individual recommendations
    (Elena Llaguri; Master Thesis; 2014)
  • Classifying and identifying customer types in an online travel community to adress customers individually
    (Steliana Sapera; Master Thesis; 2014)
  • Optimierung einer Online-Reisecommunity zur Steigerung der Nutzerpartizipation
    (Önder Sahin; Bachelor Thesis; 2013)
  • Increasing Motivation for Active Participation in Online Communities Using an Example of a Travel Platform
    (Cagla Alpsar; Master Thesis; 2013)
  • Identifikation räumlich-zeitlich auftretender Reisetrends zur Generierung individueller Reiseempfehlungen
    (Alex Fechner; Master Thesis; 2013)
  • Einsatz der sozialen Netzwerkanalyse zur Generierung automatisierter Reiseempfehlungen
    (Niklas Göltenboth; Bachelor Thesis; 2013)
  • Analyse des Marktes für Collective Knowledge Anwendungen
    (Maxi Schulz; Bachelor Thesis; 2013)
  • Identifikation und Implementierung motivationsfördernder Elemente auf einer Reiseplattform
    (Marco Theuer; Bachelor Thesis; 2013)
  • Optimierung eines Online-Reiseempfehlungssystems auf Basis der Reiseempfehlungen von Freunden
    (Sabine Ensslen; Master Thesis; 2013)
  • Optimierung eines Online-Reiseempfehlungssystems durch automatisierte Konfiguration von User-generated Content
    (Lars Oyntzen; Master Thesis; 2012)
  • Analyse des Zusammenhangs zwischen Reiseverhalten und Bildern
    (Lena Schumm; Bachelor Thesis; 2012)
  • Messung von Kundenbedürfnissen auf der Basis von Bildern
    (Nicola Baumgartner; Bachelor Thesis; 2012)
  • Optimierung eines Online-Reiseempfehlungssystems durch maschinelles Lernen
    (Sara Mayer; Bachelor Thesis; 2012)
  • Analyse von menschlichen Kundenempfehlungen zur Optimierung eines Online-Reiseempfehlungssystems
    (Tanja Schmid; Bachelor Thesis; 2012)
  • Concept of Travel Recommendation Application
    (Yining Chen, Xia Wu; Seminar Paper; 2011)
  • Nutzenpotentiale von Social Media Daten zur Gewinnung von Kundenwissen am Beispiel einer Online-Reiseplattform
    (Christian Burger; Diploma Thesis; 2011)
  • Measuring Customers Characteristics using Profile Photos of Online Social Networks
    (Yining Chen; Master Thesis)
  • Applying travel pictures to analyze customers´ travel behavior
    (Xia Wu; Master Thesis)
  • Social Knowledge Service für individuelle Reiseempfehlungen
    (Sabine Ensslen, Franziska Hartmann, Martha Kift, Alexander Sankowski, Veronika Wacker; Seminar Paper)