Lab Course in Summer Semester 2024:

Hands-on Recommender Systems

(Ashmi Banerjee)

News

[25.06.2024] - Final presentation schedule and room announced!

• Final presentations 15.07.2024 and 16.07.2024 from 15:00 on-site in Garching in room FMI 00.10.011.
  • Teams 1, 2, 3, and 4 will present on 15.07.2024 from 15:00 on-site in Garching
  • Teams 5, 6, and 7  will present on 16.07.2024 from 15:00 on-site in Garching
  • Attendance of all students on both days is mandatory.

[15.05.2024] - Final presentation dates announced!

[26.01.2024] - Webpage online!

Important Information

• Pre-meeting: 
  • Date and time: 05.02.2024 at 15:00
  • Format: Zoom
  • Slides: TBD
• Registration: using the matching system
• Duration: 15.04.2024 - 16.07.2024 
• 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 send your CV and a short motivation statement (200-250 words) to ashmi.banerjee[at]tum[dot]de (cc: Stefan.Neubig[at]outdooractive.com) 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 mandatory milestone presentations 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 in July TBD
• Information on the Presentations
  • Duration is 25-30 minutes (including Q&A) for final presentation and 10-15 mins (including Q&A) for milestone 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.

Procedure

• Pre-meeting on 05.02.2023 at 15:00, over  Zoom
• Introductory Lectures:
  • Recommender Systems: Intro
  • Recommender Systems using Knowledge-graphs (?)
• [Tentative] Regular milestone meetings from 13:00

Meeting Schedule

These are the tentative meeting dates and are subjected to change.
All meetings except final presentations take place online through Zoom on the following dates from 13:00.

All meetings take place over Zoom.

• Final presentations 15.07.2024 and 16.07.2024 from 15:00 on-site in Garching in room FMI 00.10.011.
  • Teams 1, 2, 3, and 4 will present on 15.07.2024 from 15:00 on-site in Garching
  • Teams 5, 6, and 7  will present on 16.07.2024 from 15:00 on-site in Garching
  • Attendance of all students on both days is mandatory.
• We plan to hold all lectures and meetings mostly online with some optional in-person meetings!
  EXCEPT FOR THE FINAL PRESENTATION TBD WHICH WILL BE HELD ON-SITE IN GARCHING.
• Attendance of lectures and meetings is mandatory.

Prerequisites

• 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)

Contact

• Ashmi Banerjee
• Stefan Neubig (Outdooractive)