Your SlideShare is downloading. ×
0
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Je t’aime… moi non plus: reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration

8,306

Published on

This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia …

This presentation will be a live exchange of ideas & arguments, between a representative of a start up working on agricultural information management and discovery, and a representative of academia that has recently completed his PhD and is now leading a young and promising research team.

The two presenters will focus on the case of a recommendation service that is going to be part of a web portal for organic agriculture researchers and educators (called Organic.Edunet), which will help users find relevant educational material and bibliography. They currently develop this as part of an EU-funded initiative but would both be interested to find a way to further sustain this work: the start up by including this to the bundle of services that it offers to the users of its information discovery packages, and the research team by attracting more funding to further explore recommendation technologies.

The start up representative will describe his evergoing, helpless and aimless efforts to include a research activity on recommender systems within the R&D strategy of the company, for the sakes of the good-old-PhD-times. And will explain why this failed.

The academia representative will describe the great things that his research can do to boost the performance of recommendation services in such portals. And why this does-not-work-yet-operationally because he cannot find real usage data that can prove his amazing algorithm outside what can be proven in offline lab experiments using datasets from other domains (like MovieLens and CiteULike).

Both will explain how they started working together in order to design, experimentally test, and deploy the Organic.Edunet recommendation service. And will describe their expectations from this academic-industry collaboration. Then, they will reflect on the challenges they see in such partnerships and how (if) they plan to overcome them.

Published in: Education, Technology
0 Comments
3 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
8,306
On Slideshare
0
From Embeds
0
Number of Embeds
7
Actions
Shares
0
Downloads
3
Comments
0
Likes
3
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • From data cultivation to data blossom , the Agricultural Data platform is an end-to-end modular solution that can transform data into meaningful services. The agricultural data are harvested from diverse sources and after they enrichment are published through a set of web services to external systems. The enrichment of data includes: improvement of data descriptions annotation of data with ontologies translation of data descriptions The enrichment of the data allows the development of high quality services for specific agricultural communities. Publishing is responsible for the exposure of agricultural data in a form that can be used a) for the development of data discovery services b) authoring services and c) analytics dashboards to track and study how the agricultural data are used.
  • Transcript

    • 1. Je t'aime... moi non plus Nikos Manouselis, Agro-Know Technologies (Greece) Christoph Trattner, Know-Center (Austria)
    • 2. Nikos Manouselis, Agro-Know Technologies (Greece) Christoph Trattner, Know-Center (Austria) reporting on the opportunities, expectations and challenges of a real academic-industrial collaboration …blah blah
    • 3. the characters
    • 4. Nikos • MSc, MΕng, PhD • >140 pubs • 1 post-doc • 1 hybrid research position • 5 rejected faculty applications • Agro-Know as a research hybrid
    • 5. “meaningful services around high-quality agricultural data pools” http://www.agroknow.gr
    • 6. Unorganized Content in local and remote sites Widgets Authoring services Data Discovery Services Analytics services Agricultural Data Platform Ingestion Translat e Publishing Harvesting BlossomCultivation Organized and structured Content in local and remote DBs Educational Geographical Bibliographic Enrichment Aggregate data from diverse sources Work with different type of data Prepare data for meaningful services Educational Bibliographic knowledge aggregation and sharing solutions
    • 7. Christoph Born: 24.2.1980, Graz, Austria •Research Field: Social Computing (so basically my research is centered around Social Networks Analysis, Social (Semantic) System design and Social information access) •Education: Ph.D. („Dr. techn.“) in Computer Science and a MSc & BSc in Telematics from Graz University of Technology •Publications (since 2009) : 5 Journals, 24 Conf. Papers, 2 Book Chapter, Publications in for example: WWW, HT, ICWE, Wikisym, SocialCom, ASONAM, etc. •Currently, I am working as Head of the Social Semantic Research Group and Deputy division manager at the Know-Center, in Graz Austria Contact: Email: trattner.christoph@gmai.com Web: http://christophtrattner.info Twitter: @ctrattner
    • 8. Christoph’s team • 1 Post Doc, 5 Pre Docs (1 more will join in Sept. ) • 2 MSc student • 1 BSc student DI. Dieter Theiler DI. Dominik Kowald Mag. Peter Kraker Mag. Sebastian Dennerlein Dr. Elisabeth Lex Mag. Matthias Rella
    • 9. Christoph’s collaborators
    • 10. Organic.Edunet • outcome of EU project “Organic.Edunet” of eContent+ programme (2007-2010) • based on network of >10 content providers • portal maintained & updated by Agro-Know and an academic partner (anAP) • evolved through EU project “Organic.Lingua” (2011-2014) in collaboration with K-C and anAP
    • 11. Organic.Edunet Recommendation • social navigation module exposed through API – content-based recommendation using tags on resources – user-based collaborative filtering using multi- criteria ratings • recommendation of relevant resources within user’s profile – well-hidden, never used – module API developed & supported by Agro-Know – UI & features developed & supported by anAP
    • 12. desired: Organic.Edunet “Suggest” • a real content discovery service suggesting resources to users – interactions used as input to train system – personalised vs. non-personalised version
    • 13. desired: explore further • personalising suggestions of related content when users view an item
    • 14. Agro-Know’s perspective • a service that can become a plug-and-play product – working on top of recommendation API – reusable in all agDiscovery services (sites, portals, apps) • a service that works, well – tested performance, correct parameters for algorithms in each context – tested & adaptable UI, to be reused in several deployments • a service bundle that we can sell to our clients
    • 15. Nikos’ perspective • experiment with multi-criteria recommendation – continue work that started in PhD – visualisation & UI challenges – find someone to try-that-interesting-idea • take advantage of large user base & lots of data – Organic.Edunet dataset: ratings & tags already collected – expand to federated data sources of social data • keep publishing, but not keep on doing research experiments
    • 16. Christoph’s & KC’s perspective • Why is this cooperation valuable for us/me? – Typically it is not too easy to get access to real user data.. • Test algorithms not only “offline” but also online – Currently, we are just playing around with offline experiments • Test interfaces not only in lab studies – Currently, we are evaluating our interfaces just with expert interviews or with lab studies • Work towards second doctorial thesis that lies in the context of recommending “things” (people, resources, annotations) in social semantic networks
    • 17. the plan
    • 18. bringing it all together • major activities to take place in next 9 months –offline experiments using existing dataset & exploring various algorithmic options [summer’13] –online experiments exploring various service options [autumn’13] –final service deployment [winter’13-’14]
    • 19. evaluation experiments (1/2) • evaluating algorithms –offline experiment running different algorithms over offline data that have user preferences –online experiment with single interface with back end recommendation engine interchanging between algorithm variations
    • 20. evaluation experiments (2/2) • evaluating different visualisations – simple suggested list of resources – simple tag-cloud based faceted browsing – cluster-based bubble interface for browsing bases on themes • evaluating data availability/coverage – one interface with selected algorithm with backend selector that will interchange item catalogue dataset
    • 21. research outcomes • conference publications to make K-C happy – ACM RecSys’14 – ACM HT’14 • journal publication to make all happy – ACM TIST Special Issue on Recommender System Benchmarking
    • 22. the challenges
    • 23. Nikos’ perspective • productizing & selling – bundle of services together with K-C or Agro- Know’s product? – business & costing model? • time – research mentoring is a luxury for a start-up CEO – should eventually lead to an added-value product – creates bias in product development process (what if this idea should simply die?) • trust: what if they are yet-another-anAP?
    • 24. Christoph‘s perspective • Time: Tight timeline – according to Giannis (our project coordinator) services should be done by Sept.  – Not much room for failure
    • 25. Christoph‘s perspective
    • 26. Christoph‘s perspective • Data: Sparse data... – Although the portal attracts a lot of people every day (a bunch of thousands), we currently do not have the data we need to do „real“ cool personalized recommender stuff...
    • 27. Christoph‘s PerspectiveOrganic.Edunet CiteULike
    • 28. Christoph‘s perspective • Multilinguality: – Currently the portal provides documents in 42 different languages...how do we handle that? – Well, lucky us, most articles are in English language so we might handle this by providing our services just to those users? • Speed: Although our recommendations are pretty fast (almost real-time) how do we handle network delays? Maybe it is better to set up a virtual machine?
    • 29. Christoph‘s perspective • Scalability: What happens if the portal really flies off? Currently, we have almost everthing in memory – Ok we have a big server with 256GB of RAM ...and we are using Apache Mahout for some algorithms (e.g. CF), but how about the other „cool“ algorithms we have developed and that we want to test?
    • 30. Christoph‘s perspective • Sustainability & Trust: Currently, we are pretty fine with Nikos, and he likes our ideas, but what if we want to test new stuff? – Does he allow us to change our services? – Or even worse, he does not allow us to change anything!
    • 31. confession time: why do this no script!
    • 32. thank you! nikosm@agroknow.gr ctrattner@know-center.at

    ×