The opening slides for the Recommender Systems Challenge at RecSys2012

1. Recommender Systems
Challenge
ACM RecSys 2012
Dublin
September 13 2012

2. Organizers
● Nikos Manouselis ● Jannis Hermanns
- Agro-Know & ARIADNE Foundation - Moviepilot -@jannis
● Alan Said ● Katrien Verbert
- PhD student @ TU Berlin -@alansaid - KULeuven
● Domonkos Tikk ● Hendrik Drachsler
- CEO @ Gravity R&D -@domonkostikk - Open University - The Netherlands
● Benjamin Kille ● Kris Jack
PhD tudent @ TU Berlin -@bennykille - Mendeley

3. The Challenge - 2 tracks
CAMRa ScienceRec
● Previously: 2010 & 2011 ● First time
● Finding users to recom- ● Novel algorithms,
mend a movie to visualisations, services for
● moviepilot.com data paper recommendation
● live evaluation ● Mendeley data (3 datasets)
● camrachallenge.com ● Several requested data
● 4 submitted papers
● ~60 participants
● 1 submitted paper

4. What went wrong?
● Initial results indicate that RecSys Challenge was not
successful
○ measurable result: 5 submissions, 2 accepted papers + 1 accepted
presentation/talk
● Several issues encountered
○ “we downloaded the dataset but could not run extensive simulations
because it was difficult to process”
○ “we wanted to combine the dataset with live data from the platform
but we didn’t have enough user info”
○ “we used different datasets than the ones suggested because they
were easier to access/use”
○ too diverse tracks
○ unawareness / difficulty in spreading information about the
challenge

5. What went right?
Why are we all here?
● finding datasets to experiment with (especially from live,
industrial systems) instead of working with the old
"favorites"
● learning how existing algorithms can be reused (extended,
adapted, evolved) instead of coding from scratch
● finding how our algorithm (unique, novel, amazing, the
best) can be contributed to the community conceiving
designing/deploying a great recommendation service
● make a business case out of our algorithm/service
● (become rich/famous/...)

6. The real challenge
How to make such contests work, being also useful for...
● ...the data publisher [insight into what can/cannot be
done with their data]
● ...the research community [insight into new
algorithms, approaches, services + contributions to
existing frameworks/libraries]
● ...the deployed platform [insight into new services
that could work better / be more useful]
● ...everyone [create publicity/awareness]

7. Our Workshop
● Follows a simple structure similar to how
you would participate in a challenge
○ Available Data Sets
○ Existing Algorithms/Frameworks
○ New Investigated Methods
○ Prototyped and/or Deployed Services

8. Program
09:00–09:15 Welcome & intro 11:00–12:30: Real Use
09:15–10:00 Working with Data ● From a toolkit of recommendation algorithms
● The MovieLens dataset – Michael Ekstrand into a real business: the Gravity R&D experience
● Mendeley’s data and perspective on data – Domonkos Tikk
challenges – Kris Jack ● Selecting algorithms from the plista contest to
● Processing Rating Datasets for Recommender deliver plista’s ads and editorial content on
Systems’ Research: Preliminary Experience premium publisher’s websites - Torben Brodt
from two Case Studies - Giannis Stoitsis, ● Mendeley Suggest: engineering a personalised
George Kyrgiazos, Georgios Chinis, Elina article recommender system - Kris Jack
Megalou 12:30–14:30: Lunch break
10:00–10:30: Algorithms & Experiments 14:30–15:30: Frameworks, Libs & APIs
● Usage-based vs. Citation-based Methods for ● Hands-on Recommender System Experiments
Recommending Scholarly Research Articles - with MyMediaLite - Zeno Gantner
André Vellino ● Using Apache’s Mahout and Contributing to it-
● Cross-Database Recommendation Using a Sebastian Schelter
Topical Space - Atsuhiro Takasu, Takeshi ● Flexible Recommender Experiments with Lenskit
Sagara, Akiko Aizawa - Michael Ekstrand
15:30–17:30: Hands-on work