Wakoopa Recommendations Engine on AWS

•

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Menno van der Sman, Lead Engineer of Wakoopa presents at the AWS Start-Up Event - Amsterdam about their use of Amazon EC2 and S3 for their recommendations engine.

Tecnologia Negócios

Menno van der Sman
Lead Developer

Coen Stevens
Recommendation Engineer

How to get started?

Research Mathemagicians
Amazon, Netﬂix etc
Peter Tegelaar & Coen Stevens

Ludwig created
recommender system in ruby running on EC2

Challenges
when building your ﬁrst recommender system

Data
what do we have?

Usage (implicit) vs. Ratings (explicit)

• Noisy • Accurate
• Only positive • Positive and negative
feedback feedback

• Easy to collect • Hard to collect

Item-Based Collaborative Filtering
User software usage matrix
Software items

220 90 180 22

280 12 42 80

Users 175 210 210 45

165 14 35 195 13 25

100 50 185 35 190

60 65 185

Classiﬁed user software usage matrix (1, 2, 3)
Software items

3 2 2 2

3 2 1 2

Users 3 3 2 3

2 1 2 2 3 2

3 2 2 2 3

1 2 3

How do we predict the probability that I would like to use GMail?
Software items

3 2 2 2

3 2 1 2

Users 3 3 ? 2 3

2 1 2 2 3 2

3 2 2 2 3

1 2 3

Calculate the similarities between Gmail and the other software items.
Software items

3 2 2 2

3 2 1 2

Users 3 3 2 3

2 1 2 2 3 2

3 2 2 2 3

1 2 3

Similarity(Firefox, Gmail)

Calculate the similarities between Gmail and the other software items.
Gmail similarities

0.6 3 2 2 2

0.8 3 2 1 2

1.0 3 3 2 3

0.4 2 1 2 2 3 2

0.4 3 2 2 2 3

0.3 1 2 3

0.3

Calculate the predicted value for Gmail
Gmail similarities User usage

0.6 3

0.8 3

1.0

0.4 2

0.4

0.3 3

0.3

Calculate the predicted value for Gmail
Gmail similarities User usage

We take only the ‘K’ most similar items (say 2)
0.6 3

0.8 3

1.0

0.4 2

0.4

0.3 3 0.6*3 + 0.8*3
= 2.8
0.6 + 0.8 + 0.4 + 0.3
0.3

Calculate all unknown values and
show the Top-N recommendations to each user
Software items

3 2 ? 2 ? ? 2

3 2 1 ? 2 ? ?
Users 3 3 ? 2 ? 3 ?
2 1 2 2 3 2 ?
? 3 2 2 ? 2 3
? 1 ? 2 ? ? 3

Metrics
measure for success

Space complexity: O(m + Kn)

Computational complexity: O(m + n²)

Performance: Root Mean Squared Error

Evaluating the approach

Maximize
( performance

cost )
This is easy with EC2

Why EC2?

Low cost

Flexibility

Ease of use

Infrastructure
Wakoopa EC2
checkout
Repository
Computing
Application power

Database ssh tunnel
Big
Database

Want more?

http://recked.org

Time & place TBD

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In an era where artificial intelligence (AI) stands at the forefront of business innovation, Information Architecture (IA) is at the core of functionality. See “There’s No AI Without IA” – (from 2016 but even more relevant today) Understanding and leveraging how Information Architecture (IA) supports AI synergies between knowledge engineering and prompt engineering is critical for senior leaders looking to successfully deploy AI for internal and externally facing knowledge processes. This webinar be a high-level overview of the methodologies that can elevate AI-driven knowledge processes supporting both employees and customers. Core Insights Include: Strategic Knowledge Engineering: Delve into how structuring AI's knowledge base is required to prevent hallucinations, enable contextual retrieval of accurate information. This will include discussion of gold standard libraries of use cases support testing various LLMs and structures and configurations of knowledge base. Precision in Prompt Engineering: Learn the art of crafting prompts that direct AI to deliver targeted, relevant responses, thereby optimizing customer experiences and business outcomes. Unified Approach for Enhanced AI Performance: Explore the intersection of knowledge and prompt engineering to develop AI systems that are not only more responsive but also aligned with overarching business strategies. Guiding Principles for Implementation: Equip yourself with best practices, ethical guidelines, and strategic considerations for embedding these technologies into your business ecosystem effectively. This webinar is designed to empower business and technology leaders with the knowledge to harness the full potential of AI, ensuring their organizations not only keep pace with digital transformation but lead the charge. Join us to map a roadmap to fully leverage Information Architecture (IA) and AI chart a course towards a future where AI is a key pillar of strategic innovation and business success.

EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx

Earley Information Science

Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

UK Journal

As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

TrustArc

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Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

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A Call to Action for Generative AI in 2024

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Building Digital Trust in a Digital Economy Veronica Tan, Director - Cyber Security Agency of Singapore Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...

apidays

Wakoopa Recommendations Engine on AWS

2. Menno van der Sman Lead Developer Coen Stevens Recommendation Engineer

3. Mission: Discover software & games

4. Updates

5. Searching powered by

6. Recommendations Codename: Ludwig

7. How to get started? Research Mathemagicians Amazon, Netﬂix etc Peter Tegelaar & Coen Stevens Ludwig created recommender system in ruby running on EC2

8. Challenges when building your ﬁrst recommender system

9. Data what do we have? Usage (implicit) vs. Ratings (explicit) • Noisy • Accurate • Only positive • Positive and negative feedback feedback • Easy to collect • Hard to collect

10. Item-Based Collaborative Filtering User software usage matrix Software items 220 90 180 22 280 12 42 80 Users 175 210 210 45 165 14 35 195 13 25 100 50 185 35 190 60 65 185

11. Classiﬁed user software usage matrix (1, 2, 3) Software items 3 2 2 2 3 2 1 2 Users 3 3 2 3 2 1 2 2 3 2 3 2 2 2 3 1 2 3

12. How do we predict the probability that I would like to use GMail? Software items 3 2 2 2 3 2 1 2 Users 3 3 ? 2 3 2 1 2 2 3 2 3 2 2 2 3 1 2 3

13. Calculate the similarities between Gmail and the other software items. Software items 3 2 2 2 3 2 1 2 Users 3 3 2 3 2 1 2 2 3 2 3 2 2 2 3 1 2 3 Similarity(Firefox, Gmail)

14. Calculate the similarities between Gmail and the other software items. Gmail similarities 0.6 3 2 2 2 0.8 3 2 1 2 1.0 3 3 2 3 0.4 2 1 2 2 3 2 0.4 3 2 2 2 3 0.3 1 2 3 0.3

15. Calculate the predicted value for Gmail Gmail similarities User usage 0.6 3 0.8 3 1.0 0.4 2 0.4 0.3 3 0.3

16. Calculate the predicted value for Gmail Gmail similarities User usage We take only the ‘K’ most similar items (say 2) 0.6 3 0.8 3 1.0 0.4 2 0.4 0.3 3 0.6*3 + 0.8*3 = 2.8 0.6 + 0.8 + 0.4 + 0.3 0.3

17. Calculate all unknown values and show the Top-N recommendations to each user Software items 3 2 ? 2 ? ? 2 3 2 1 ? 2 ? ? Users 3 3 ? 2 ? 3 ? 2 1 2 2 3 2 ? ? 3 2 2 ? 2 3 ? 1 ? 2 ? ? 3

18. Metrics measure for success Space complexity: O(m + Kn) Computational complexity: O(m + n²) Performance: Root Mean Squared Error

19. Evaluating the approach Maximize ( performance cost ) This is easy with EC2

20. Why EC2? Low cost Flexibility Ease of use

21. Infrastructure Wakoopa EC2 checkout Repository Computing Application power Database ssh tunnel Big Database

22. Want more? http://recked.org Time & place TBD

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