Laat dit een waarschuwing zijn. Je presentatie kan helemaal de mist in gaan door een slechte powerpoint. Dit is een voorbeeld van hoe het niet moet. Let op: de faal-animaties doen het bij deze slideshare helaas niet...
Music Recommendation and Discovery in the Long TailOscar Celma
Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.
Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations.
In this Thesis we stress the importance of the user's perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution.
The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.
Laat dit een waarschuwing zijn. Je presentatie kan helemaal de mist in gaan door een slechte powerpoint. Dit is een voorbeeld van hoe het niet moet. Let op: de faal-animaties doen het bij deze slideshare helaas niet...
Music Recommendation and Discovery in the Long TailOscar Celma
Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.
Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations.
In this Thesis we stress the importance of the user's perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution.
The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.
The project was taken as a part of consulting exercise in alliance with Elidea. My role was to address the current challenge at Xeeva with an integrated solution and propose a mutually beneficent business model to ensure long term engagement of Elidea with Xeeva.
I was able to weave a solution which featured
- One Snap Deployment
- Global standard compliance
- Process & Workflow agnostic
- Continuous Updates
- Robust Security
- Scalability on the Cloud
***Challenge:
Xeeva would do product releases in sprints of every 2 months. The "Product" and "Training & Development" teams were disconnected with no common platform in place except JIRA. JIRA with its enormous capabilities was still being underutilized with just being a Bug Tracking and Product backlog prioritization tool.
The whole process of documenting features, creation of user guides and process of updating features & changes to the product suite was manual and exhaustive. This was not limited and also included scheduling Customer trainings, on the Product suite and training for new releases were scheduled, manual and resource intensive.
***Challenge Accepted:
Elidea decided to transform this process of disseminating training and sharing knowledge. I already had worked in Xeeva and I knew about the gaps inside various teams. Elidea and I decided work on the core problem. We were able to come up with a blended solution utilizing open source components which were cost effective, scalable and much more secure with respect to other alternatives.
The solution aimed to deal with the problem from the core by sharing Knowledge among teams via "Knowledge Base" which formed the crux of the "Collaborative Learning Environment - CLE". "Knowledge Base" was integrated with "Learning Management System" (LMS).
The CLE allows the "Product Team" & "Training and Development" Team (and other teams as well) to be connected via common platform and share validated knowledge. This knowledge is then used as assets and feeded into LMS where customers can automate their learnings and the process becomes less resource exhaustive.
***About Xeeva:
Xeeva is driving the development and delivery of the next generation of intelligent procurement and financial solutions. Xeeva’s technology is used in over 40 countries and is available in 18 languages. The company’s end to end technology suite includes Sourcing, Procure to Pay, Supplier Collaboration, Financial Collaboration and Extended Enterprise solutions. You can learn more about Xeeva’s singular focus on driving immediate and sustainable results for its customers by visiting www.xeeva.com
***About Elidea:
Elidea is a training and development company. Elidea provides innovative learning & development solutions/products. Elidea also offers services around T&D which expedites the process of on boarding employees/vendors alike on the LMS Platforms.
Thank you
Gagan Bhalla
Prezentacja ma na celu przedstawienie Google Glass – gadżetu, który niebawem pojawi się w sprzedaży detalicznej i otworzy nowe możliwości przed użytkownikami. Jednocześnie twórcy oprogramowania na platformy mobilne staną przed nowymi wyzwaniami związanymi m.in. z projektowaniem interfejsów i sposobie interakcji z urządzeniem. Podczas prezentacji zostaną także omówione podstawowe zagadnienia związane z tworzeniem natywnych aplikacji na Google Glass oraz framework’a Mirror API.
Wideo na http://www.netcamp.pl
Czy samochód może być wearable? Czym tak naprawdę będzie Automotive-Human-Machine-Interface? Kiedy rozszerzona rzeczywistość zastąpi człowieka? Czy autonomiczne samochody połączą się w grid i jakie to ma konsekwencje? Jak rozwijać się będą systemy nawigacji kolejnej generacji? Prezentacja pokaże najnowsze trendy w branży Automotive i projekty realizowane w REC-Global.
Wideo na http://www.netcamp.pl
Enkele Problemen met DCT gebaseerde Ray TracerDavy Debacker
Een aantal problemen uit de doeken gedaan met betrekking tot mijn implementatie van een adaptieve ray tracer gebaseerd op een DCT en Wavelet transformatie.
The project was taken as a part of consulting exercise in alliance with Elidea. My role was to address the current challenge at Xeeva with an integrated solution and propose a mutually beneficent business model to ensure long term engagement of Elidea with Xeeva.
I was able to weave a solution which featured
- One Snap Deployment
- Global standard compliance
- Process & Workflow agnostic
- Continuous Updates
- Robust Security
- Scalability on the Cloud
***Challenge:
Xeeva would do product releases in sprints of every 2 months. The "Product" and "Training & Development" teams were disconnected with no common platform in place except JIRA. JIRA with its enormous capabilities was still being underutilized with just being a Bug Tracking and Product backlog prioritization tool.
The whole process of documenting features, creation of user guides and process of updating features & changes to the product suite was manual and exhaustive. This was not limited and also included scheduling Customer trainings, on the Product suite and training for new releases were scheduled, manual and resource intensive.
***Challenge Accepted:
Elidea decided to transform this process of disseminating training and sharing knowledge. I already had worked in Xeeva and I knew about the gaps inside various teams. Elidea and I decided work on the core problem. We were able to come up with a blended solution utilizing open source components which were cost effective, scalable and much more secure with respect to other alternatives.
The solution aimed to deal with the problem from the core by sharing Knowledge among teams via "Knowledge Base" which formed the crux of the "Collaborative Learning Environment - CLE". "Knowledge Base" was integrated with "Learning Management System" (LMS).
The CLE allows the "Product Team" & "Training and Development" Team (and other teams as well) to be connected via common platform and share validated knowledge. This knowledge is then used as assets and feeded into LMS where customers can automate their learnings and the process becomes less resource exhaustive.
***About Xeeva:
Xeeva is driving the development and delivery of the next generation of intelligent procurement and financial solutions. Xeeva’s technology is used in over 40 countries and is available in 18 languages. The company’s end to end technology suite includes Sourcing, Procure to Pay, Supplier Collaboration, Financial Collaboration and Extended Enterprise solutions. You can learn more about Xeeva’s singular focus on driving immediate and sustainable results for its customers by visiting www.xeeva.com
***About Elidea:
Elidea is a training and development company. Elidea provides innovative learning & development solutions/products. Elidea also offers services around T&D which expedites the process of on boarding employees/vendors alike on the LMS Platforms.
Thank you
Gagan Bhalla
Prezentacja ma na celu przedstawienie Google Glass – gadżetu, który niebawem pojawi się w sprzedaży detalicznej i otworzy nowe możliwości przed użytkownikami. Jednocześnie twórcy oprogramowania na platformy mobilne staną przed nowymi wyzwaniami związanymi m.in. z projektowaniem interfejsów i sposobie interakcji z urządzeniem. Podczas prezentacji zostaną także omówione podstawowe zagadnienia związane z tworzeniem natywnych aplikacji na Google Glass oraz framework’a Mirror API.
Wideo na http://www.netcamp.pl
Czy samochód może być wearable? Czym tak naprawdę będzie Automotive-Human-Machine-Interface? Kiedy rozszerzona rzeczywistość zastąpi człowieka? Czy autonomiczne samochody połączą się w grid i jakie to ma konsekwencje? Jak rozwijać się będą systemy nawigacji kolejnej generacji? Prezentacja pokaże najnowsze trendy w branży Automotive i projekty realizowane w REC-Global.
Wideo na http://www.netcamp.pl
Enkele Problemen met DCT gebaseerde Ray TracerDavy Debacker
Een aantal problemen uit de doeken gedaan met betrekking tot mijn implementatie van een adaptieve ray tracer gebaseerd op een DCT en Wavelet transformatie.
16. Adaptieve en Progressieve RayTracing DCT gebaseerde algoritme Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Frequentie voorstelling Academiejaar2008-2009 8
17. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Verdeel in 8x8 Bereken DCT Neem X mpb Academiejaar2008-2009 Neem X mpb Bereken DCT Bereken gemiddeld gekwadrateerd verschil tussen DCTs 9
18. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Neem X monsters in B Bereken DCT Neem blok B met grootste verschil Academiejaar2008-2009 Zolang niet aan stopconditie voldaan 10
19. Adaptieve en Progressieve RayTracing DCT gebaseerde algoritme Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Verdeling van de monsters Academiejaar2008-2009 11
20. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 12
21. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 13
22. Adaptieve en Progressieve RayTracing DCT gebaseerde algoritme Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Probleem Academiejaar2008-2009 14
23. Adaptieve en Progressieve RayTracing DCT gebaseerde algoritme Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Probleem Academiejaar2008-2009 15
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25. Adaptieve en Progressieve RayTracing DCT gebaseerde algoritme Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Verdeling van de monsters Academiejaar2008-2009 17
26. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 18
27.
28. Adaptieve en Progressieve RayTracing DCT gebaseerde algoritme Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Verdeling van de monsters Academiejaar2008-2009 20
29. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 21
32. Adaptieve en Progressieve RayTracing Haar Wavelet algoritme Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Frequentie voorstelling Academiejaar2008-2009 23
33. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 … … … … 24
34. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Verdeel het beeld Neem X monsters Academiejaar2008-2009 Voeg wortel toe aan gesorteerde lijst Bereken wavelet coëfficiënten 25
35. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Neem element N met grootste coëfficiënt uit de lijst Voeg de kinderen van N toe aan de lijst Academiejaar2008-2009 Zolang niet aan stopconditie voldaan Update coëfficiënten Neem X monsters in oppervlak van N 26
36. Adaptieve en Progressieve RayTracing Haar waveletalgoritme Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Verdeling van de monsters Academiejaar2008-2009 27
37. Adaptieve en Progressieve RayTracing Haar waveletalgoritme Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Subpixelniveau Academiejaar2008-2009 28
38. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 29
39. Adaptieve en Progressieve RayTracing Resultaten Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Haar Wavelet DCT Academiejaar2008-2009 30
40. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 31
41. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 32
42. Adaptieve en Progressieve RayTracing Resultaten Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Haar Wavelet DCT Academiejaar2008-2009 33
43. Adaptieve en Progressieve RayTracing Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 34
44. Adaptieve en Progressieve RayTracing Hartelijk dank voor uw aandacht Master Informatica Masterproef Davy Debacker Promotor Prof. dr. ir. Ph. Dutré Academiejaar2008-2009 35