SlideShare utilise les cookies pour améliorer les fonctionnalités et les performances, et également pour vous montrer des publicités pertinentes. Si vous continuez à naviguer sur ce site, vous acceptez l’utilisation de cookies. Consultez nos Conditions d’utilisation et notre Politique de confidentialité.
SlideShare utilise les cookies pour améliorer les fonctionnalités et les performances, et également pour vous montrer des publicités pertinentes. Si vous continuez à naviguer sur ce site, vous acceptez l’utilisation de cookies. Consultez notre Politique de confidentialité et nos Conditions d’utilisation pour en savoir plus.
Recommender Systems Challenge ACM RecSys 2012 Dublin September 13 2012
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
The Challenge - 2 tracksCAMRa 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
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
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/...)
The real challengeHow 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]
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
Program09:00–09:15 Welcome & intro 11:00–12:30: Real Use09:15–10:00 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 break10:00–10:30: 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