The document discusses building a recommender system using collaborative filtering approaches. It describes collecting usage and rating data, calculating item-item and user-user similarities, making predictions for unknown values using k-nearest neighbors, and evaluating the system using measures like precision, recall and root mean squared error. Implementation details like programming languages, databases and cloud infrastructure are also summarized.
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
What really are recommendations engines nowadays?
This presentation introduces the foundations of recommendation algorithms, and covers common approaches as well as some of the most advanced techniques. Although more focused on efficiency than theoretical properties, basics of matrix algebra and optimization-based machine learning are used through the presentation.
Table of Contents:
1. Collaborative Filtering
1.1 User-User
1.2 Item-Item
1.3 User-Item
* Matrix Factorization
* Stochastic Gradient Descent (SGD)
* Truncated Singular Value Decomposition (SVD)
* Alternating Least Square (ALS)
* Deep Learning
2. Content Extraction
* Item-Item Similarities
* Deep Content Extraction: NLP, CNN, LSTM
3. Hybrid Models
4. In Production
4.1 Problematics
4.2 Solutions
4.3 Tools
Personalized Page Generation for Browsing RecommendationsJustin Basilico
Talk from First Workshop on Recommendation Systems for TV and Online Video at RecSys 2014 in Foster City, CA on 2014-10-10 about how we personalize the layout of the Netflix homepage to make it easier for people to browse the recommendations to quickly find something to watch and enjoy.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
In this presentation, we survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will present case studies in the space of deep latent variable models applied to recommender systems.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Tutorial on Deep Learning in Recommender System, Lars summer school 2019Anoop Deoras
I had a fun time giving tutorial on the topic of deep learning in recommender systems at Latin America School on Recommender Systems (LARS) in Fortaleza, Brazil.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Personalized Page Generation for Browsing RecommendationsJustin Basilico
Talk from First Workshop on Recommendation Systems for TV and Online Video at RecSys 2014 in Foster City, CA on 2014-10-10 about how we personalize the layout of the Netflix homepage to make it easier for people to browse the recommendations to quickly find something to watch and enjoy.
Recommender Systems represent one of the most widespread and impactful applications of predictive machine learning models.
Amazon, YouTube, Netflix, Facebook and many other companies generate an important fraction of their revenues thanks to their ability to model and accurately predict users ratings and preferences.
In this presentation we cover the following points:
→ introduction to recommender systems
→ working with explicit vs implicit feedback
→ content-based vs collaborative filtering approaches
→ user-based and item-item methods
→ machine learning and deep learning models
→ pros & cons of the methods: scalability, accuracy, explainability
In this lecture, I will first cover the recent advances in neural recommender systems such as autoencoder-based and MLP-based recommender systems. Then, I will introduce the recent achievement for automatic playlist continuation in music recommendation.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
In this presentation, we survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will present case studies in the space of deep latent variable models applied to recommender systems.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Tutorial on Deep Learning in Recommender System, Lars summer school 2019Anoop Deoras
I had a fun time giving tutorial on the topic of deep learning in recommender systems at Latin America School on Recommender Systems (LARS) in Fortaleza, Brazil.
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Past, present, and future of Recommender Systems: an industry perspectiveXavier Amatriain
Keynote for the ACM Intelligent User Interface conference in 2016 in Sonoma, CA. I start with the past by talking about the Recommender Problem, and the Netflix Prize. Then I go into the Present and the Future by talking about approaches that go beyond rating prediction and ranking and by finishing with some of the most important lessons learned over the years. Throughout my talk I put special emphasis on the relation between algorithms and the User Interface.
Recommender engines are used by more and more e-commerce businesses to help consumers finding products they are interested in. The paper describes what recommender engines are and what role they play in e-commerce. Recommender engines use various techniques that use different knowledge sources to make recommendations. The paper explains these techniques and their strengths and weaknesses. Some of the common issues that recommender systems face are discussed and possible solutions presented. Concluding examples of recommender engines in e-commerce are described. It is shown what techniques they use and how the e-businesses utilize recommendations on
their websites.
genetic algorithm based music recommender systemneha pevekar
The goal of a recommender
system is to generate meaningful recommendations to
a collection of users for items or products that might
interest them.
Many of the largest e-commerce websites are already
using recommender systems to help their customers
find products to purchase or download.
How to Build a Recommendation Engine on SparkCaserta
How to Build a Recommendation Engine on Spark was a presentation given by Joe Caserta, CEO and founder of Caserta Concepts, at @AnalyticsWeek in Boston.
Boston's Data AnalyticsStreet Conference is a 2 day packed event with thought provoking keynotes, knowledge filled sessions, intense workshops, insightful panels, and real-world case studies - engaging analytics community with latest methodologies and trends. The conference encompasses largest Speaker-to-Attendee ratio for unmatched networking and learning opportunity.
For more information on the services and solutions Caserta Concepts offers, visit our website at http://casertaconcepts.com/.
Design patterns are acknowledged as powerful conceptual tools to improve design quality and to reduce the time and cost of design
by effect of the reuse of “good” solutions. In many fields such as software engineering, web engineering, and interface design,
patterns are widely used by practitioners and are also investigated from a research perspective. Still, the concept of design pattern
has received marginal attention in the arena of user interfaces (UIs) for Recommender Systems (RSs). To our knowledge, a little
is known about the use of patterns in this specific class of applications, in spite of their increasing popularity, and no RS
specific interface pattern is available in existing pattern languages. We have performed a systematic analysis of 28 real-world RSs in
a variety of sectors, in order to: (i) discover occurrences of existing general (i.e., domain independent) UI patterns; (ii)
identify recurrent UI design solutions for RS specific features; (iii) elicit a set of new UI patterns for RS interfaces. The analysis
of patterns occurrences highlights the degree at which “good” UI design solutions are adopted in RSs for the different sectors. The
new patterns can be used by UI designers of RSs to improve the UX of their systems.
IoT Building Blocks: From Edge Devices to Analytics in the Cloud - SRV204 - A...Amazon Web Services
In this session, we explore the features and functions of AWS IoT services. We first cover AWS IoT fundamentals and our AWS Partner Network (APN) ecosystem. Then we discuss AWS IoT services in greater detail, review best practices for IoT solutions, and examine common architectural patterns. With this foundation in place, we explore a use case for IoT applications. You gain an understanding of how to start building IoT applications with AWS IoT.
We've learned a lot through doing DevOps: Every commit is automatically integrated, tested, and deployed to a staging environment. And then it only takes one push of a button and the release goes live...
Unfortunately, it's not as simple anymore when operating mobile applications: How can you quickly update your mobile software when the app store provider wants to test your software first for a few days? How can you update your configuration when your app can run offline? And how do you track down errors when the data is distributed to millions of mobile clients? Those were just some of the challenges we encountered during the operation of mobile games with millions of daily users. In this talk we will talk about the solutions we have found to address them.
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.
Building native apps with web componentsDenis Radin
In 2016 mobile web apps are still perceived as slow however performance is not a bottleneck anymore. UX is. Users are highly addicted to UI kits mobile ecosystem provide and it is amazingly hard to mimic native controls. But we can use native controls in our Web apps by placing custom tags <native-button>, <native-checkbox> etc. and still be able to use whole set of CSS to layout and wide subset to style app.
IoT Building Blocks: From Edge Devices to Analytics in the Cloud - SRV204 - A...Amazon Web Services
In this session, we explore features and functions of AWS IoT services. First we will cover AWS IoT fundamentals, review best practices for IoT solutions, and look at some common architectural patterns. Then we will dive deep into AWS IoT Analytics. We will explain how AWS IoT Analytics runs sophisticated analytics on massive volumes of IoT data and helps operationalize analyses without requiring you to build an IoT analytics platform from the ground up. You will hear from TensorIoT, an AWS IoT Analytics partner, about how they are using AWS IoT Analytics. Leave this session with an understanding of how to start building IoT applications with AWS IoT.
In this workshop, you learn about the different components of AWS IoT Analytics. You have the opportunity to configure AWS IoT Analytics to ingest data from AWS IoT Core, enrich the data using AWS Lambda, visualize the data using Amazon QuickSight, and perform machine learning using Jupyter Notebooks. Join us, and build a solution that helps you perform analytics on appliance energy usage in a smart building and forecast energy utilization to optimize consumption.
Our Favorite Admin Features in Cognos Analytics 11.1Senturus
Topics include: improved integration of admin capabilities, the new more efficient reporting UI, the ability to pin the context toolbar, pros and cons of the new visualizations, more robust data modules and update on releases and a roadmap for 11.0 and 11.1. View the video recording and download this deck at: https://www.senturus.com/resources/our-favorite-admin-features-in-cognos-analytics-11-1/.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
PredictionIO - Building Applications That Predict User Behavior Through Big D...predictionio
Building Applications That Predict User Behavior Through Big Data Using Open-Source Technologies
Presented by PredictionIO at Big Data TechCon (Oct 17, 2013)
GluonNLP is a deep learning toolkit for Natural Language Processing. These slides covers the motivation behind the creation of the toolkit and what is available in it. Go try it at https://gluon-nlp.mxnet.io!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
11. Data
what do we have?
Usage (implicit) Ratings (explicit)
vs.
• •
Noisy Accurate
• •
Only positive feedback Positive and negative
feedback
• •
Easy to collect Hard to collect
12. Data
what do we use?
• Active users (Tracker activity in the past month): ~9.000
• Actively used software items (in the past month): ~10.000
• We calculate recommendations for each OS together with
Web applications separately
13. Recommender system methods
Collaborative recommendations: The user will be
recommended items that people with similar tastes and
preferences liked (used) in the past
• Item-based collaborative filtering
• User-based collaborative filtering (we only use for
calculating user similarities to find people like you)
• Combining both methods
22. K-nearest neighbor approach
Gmail similarities
• Performance vs quality
0.6
• We take only the ‘K’ most similar items (say 4)
0.8
• Space complexity: O(m + Kn)
0.4
•
0.4
Computational complexity: O(m + n²)
0.3
0.3
23. Calculate the predicted value for Gmail
Gmail similarities User usage
1
0.6
1
0.8
1
0.4
0.4
1
24. Calculate the predicted value for Gmail
Gmail similarities User usage
0.9
0.6
Usage correction,
0.8
0.8
more usage results
in a higher score [0,1]
0.6
0.4
0.4
0.2
25. Calculate the predicted value for Gmail
Gmail similarities User usage
0.9
0.6
0.8
0.8
0.6
0.4
0.4
0.2
(0.6 * 0.9) + (0.8 * 0.8) + (0.4 * 0.6)
= 0.82
0.6 + 0.8 + 0.4 + 0.4
26. Calculate the predicted value for Gmail
• User feedback
Gmail similarities User usage
• Contacts usage
0.9
0.6
• Commercial vs Free
0.8
0.8
0.6
0.4
0.4
0.2
(0.6 * 0.9) + (0.8 * 0.8) + (0.4 * 0.6)
= 0.82
0.6 + 0.8 + 0.4 + 0.4
27. Calculate all unknown values and
show the Top-N recommendations to each user
Software items
? ?
?
1 1 1 1
?1??
1 1 1
?1?1?
Users 1 1
?1111?
1
?111?11
?1?1??1
28. Explainability
Why did I get this recommendation?
• Overlap between the item’s (K) neighbors and your usage
30. Applying inverse user frequency
log(n/ni): ni is the number of users that uses item i and n is
the total number of users in the database
0.1 0.2 0 0.4 0 0.4 0
0.1 0.2 0.6 0 0.8 0 0
0.1 0.2 0 0.4 0 0.4 0
0.1 0.2 0.6 0.4 0.8 0.4 0
Cosine Similarity(Coen, Menno)
0 0.2 0.6 0.4 0 0.4 0.2
0 0.2 0 0.4 0 0 0.2
The fact that you both use Textmate tells you more than
when you both use firefox
33. Performance
measure for success
• Cross-validation: Train-Test split (80-20)
• Precision and Recall:
- precision = size(hit set) / size(total given recs)
- recall = size(hit set) / size(test set)
• Root mean squared error (RMSE)
34. Implementation
• Ruby Enterprise Edition (garbage collection)
• MySQL database
• Built our own c-libraries
• Amazon EC2:
- Low cost
- Flexibility
- Ease of use
• Open source
35. Future challenges
• What is the best algorithm for Wakoopa? (or you)
• Reducing space-time complexity (scalability):
- Parallelization (Clojure)
- Distributed computing (Hadoop)
