Lab Course in Summer Semester 2023:

Hands-on Recommender Systems

(Ashmi Banerjee)


[18.01.2023] - Webpage online!

Important Information

  • Pre-meeting
    • Date and time: 07.02.2023 at 16:00
    • Format: Zoom
    • Slides: TBD
  • Registration: using the matching system
  • Duration: 24.04.2023 - 18.07.2023 
  • ECTS: 10.
  • Capacity: 24
  • Concept: Teams of 3 students will have to come up with an application of Tourism Recommender Systems, implement and deploy the prototype on any popular existing infrastructure.
  • Students in groups of 3 people are required to come up with a potential use-case on Tourism Recommender Systems and implement it over the lecture period of 14 weeks.
    • You can either apply together with your team or build your own team during the initial phase. If you already have a team of your own you can write a short motivation statement to ashmi.banerjee[at]tum[dot]de with the request to match you in the same group. There is no guarantee that you will be matched with your preferred group-mates by the matching system but this definitely increases your chances. Final team formation will be completed on the first day of the lab course.
    • You are expected to come up with an idea that uses Recommender Systems in Travel and Tourism and implement it
    • Since this course is a collaboration with Outdooractive, there will be the opportunity to use data from Outdooractive. However, it is encouraged that students use a combination of multiple data sources to build their system.
    • In the end they will have to present their results during a final presentation.
    • There will be bi-weekly meetings where each group is required to present their intermediate progress for group feedback/discussion in 15mins (10 mins presentation + 5 mins Q&A).
    • Final presentations will be held on 17.07.2023 and 18.07.2023 from 15:00 in person at Garching in Room: 00.10.011, Fachbereich (5610.EG.011)
  • Information on the Presentations
    • Duration is 25-30 minutes (including Q&A) for final presentation and 10-15 mins (including Q&A) for bi-weekly ones
    • The talk should be given freely, i.e. not completely read out from a script (in English)
    • You should present slides electronically in any format (e.g. Powerpoint or PDF)
    • You can use the TUM powerpoint template or your own format for the slides
  • Evaluation criteria:
    • GitHub repository with correct accesses and weekly presentations
    • Performance during presentation(s)
    • Final presentation 

Course Description

In this course, will emphasize on the hands-on process of developing Tourism Recommender Systems from inception to production.

Travellers today rely on the Internet for information to plan their trips. However, the explosive amount of available digital information brings the potential challenge of information overload. Tourism Recommender Systems play an effective tool for handling this information overload by helping end users find information of their interest and preference.

In this course, students will work in teams of 3, on a hands-on project, giving them the opportunity to gain experience in implementing and evaluating recommender systems using real-world data and tools. Since this course is a collaboration with Outdooractive, there will be the opportunity to use data from Outdooractive. However, it is encouraged that in addition to the provided data, the students use a combination of multiple data sources to build their system.

By the end of the course, students will have a deep understanding of how to design and implement effective recommender systems for the tourism industry, and be able to apply this knowledge to their own projects and work in the field.


  • Pre-meeting on 07.02.2023 at 16:00, over  Zoom
  • Introductory Lectures:
    • Recommender Systems: Intro
    • Recommender Systems using Knowledge-graphs (?)
  • [Tentative] Regular meetings from 15:00

Meeting Schedule

  • 17.04.2023 - organisational issues + team formation +  project proposals + Outdooractive
  • 19.04.2023 - Lecture
  • 24.04.2023 - Lecture
  • 26.04.2023 - Project Proposals (5 mins per team)
  • 03.05.2023 - group project updates (Team 1,2,3,4) 
  • 08.05.2023 - group project updates (Team 5,6,7,8)
  • 10.05.2023 - group project updates (Team 1,2,3,4)
  • 15.05.2023 - group project updates (Team 5,6,7,8)
  • 22.05.2023 - [optional] onsite debugging session
  • 24.05.2023 - group project updates (Team 1,2,3,4)
  • 05.06.2023 - group project updates (Team 5,6,7,8)
  • 12.06.2023 - [optional] onsite debugging session
  • 19.06.2023 - group project updates (Team 1,2,3,4)
  • 21.06.2023 - group project updates (Team 5,6,7,8)
  • 26.06.2023 - [optional] onsite debugging session
  • 03.07.2023 - group project updates (Team 1,2,3,4)
  • 05.07.2023 - group project updates (Team 5,6,7,8)

All meetings take place over Zoom.

  • Final presentations on 17.07.2023 and 18.07.2023, from 15:00 in Garching in Room: 00.10.011, Fachbereich (5610.EG.011)
  • We plan to hold all lectures and meetings mostly online with some optional in-person meetings!
  • Attendance of lectures and meetings is mandatory.


  • Proficiency in programming using Python (required)
  • Good understanding of version controlling such as Git (required)
  • Basic data analysis skills (required)
  • Understanding of Recommender Systems, Deep Learning (good to have but not necessary)