Yelper Helper Concept
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This document describes Yelper Helper, a personalized review engine for Yelp users. It aims to determine the usefulness of new reviews and compute user similarity. It does this by using a data structure containing over 363,000 reviews from over 112,000 users of 3,536 businesses. It predicts the number of "useful" tags for a review using a zero-inflated Poisson regression model after performing feature selection and model selection. User similarity is computed through a similarity matrix calculating Euclidean distance between user-taste and restaurant-category matrices generated through collaborative filtering of user ratings data.
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This document proposes a personalized review engine called Yelper Helper that uses collaborative filtering and predictive modeling to determine the usefulness of new reviews for Yelp users. It does this by computing user similarity based on a user-taste matrix and using a zero-inflated Poisson regression model to predict the number of "useful" tags a review will receive using attributes of the user, review, and business. The system was tested on a dataset of over 360,000 reviews from Yelp's Las Vegas dataset.
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This document proposes a personalized review engine called Yelper Helper that consists of two parts: 1) predicting the usefulness of new reviews using a zero-inflated Poisson regression model on review attributes, user attributes, and business attributes; and 2) computing user similarity based on a use-taste matrix and restaurant-category matrix to uncover user taste preferences using collaborative filtering and generate a similarity matrix. The goal is to address the problem that around 75% of Yelp reviews have zero useful tags by taking different approaches to predict review usefulness and compute user similarity to better personalize recommendations for users.
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This document proposes a personalized review engine called Yelper Helper that consists of two parts: 1) predicting the usefulness of new reviews using a zero-inflated Poisson regression model on user, business, and review attributes from the Yelp dataset; and 2) computing user similarity through collaborative filtering to uncover taste preferences using a use-taste matrix and restaurant-category matrix. The goal is to address the problem that around 75% of Yelp reviews have zero useful tags by taking different modeling approaches to address issues in the data for similarity and prediction.
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1) Declining quality of education as seen in poor test scores and a high percentage of students and teachers failing certification exams.
2) Large disparities in educational achievement based on socioeconomic status, with disadvantaged students having high dropout rates.
3) Underfunding of education relative to other ASEAN countries and low spending per student that has declined in real terms.
4) A mismatch between the skills taught and actual job requirements that leads to educated unemployment.
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“If you want a great site, you’ve got to test.” - Steve Krug, Usability Expert
At it’s most basic, usability is about insuring something, such as a website, works well. Without usability testing results, design and functionality decisions are based on opinion. Despite the understood importance, many organizations believe usability testing is too expensive and time consuming to fit their budget and schedule. We will show how usability tests can be performed both quickly and inexpensively using popular DIY usability techniques. We will cover both analog and online tools for user surveys, card sorting, tree testing, first click testing, and user testing. You don’t need a lot of participants either—many valuable tests may be performed with as few as five subjects.
Takeaways:
What is usability testing?
What, when, and who to test?
List of free and/or inexpensive usability tools
How to plan and run your own usability test?
What to do with the data when you are done?
Similar to Yelper Helper Concept (20)
Modern Perspectives on Recommender Systems and their Applications in Mendeley
Modern Perspectives on Recommender Systems and their Applications in Mendeley
Get It Right the First Time Through Cheap and Easy DIY Usability Testing - Dr...
Get It Right the First Time Through Cheap and Easy DIY Usability Testing - Dr...
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Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
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* Step-by-step implementation guide
* Live demos with code snippets
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* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
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CAKE: Sharing Slices of Confidential Data on Blockchain
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Mind map of terminologies used in context of Generative AI
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Microsoft - Power Platform_G.Aspiotis.pdf
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
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GraphRAG for Life Science to increase LLM accuracy
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June Patch Tuesday
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UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Essentials of Automations: The Art of Triggers and Actions in FME
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Recently uploaded (20)
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Presentation of the OECD Artificial Intelligence Review of Germany
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20240609 QFM020 Irresponsible AI Reading List May 2024
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CAKE: Sharing Slices of Confidential Data on Blockchain
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Essentials of Automations: The Art of Triggers and Actions in FME
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Yelper Helper Concept
- 1. •Personalized Review Engine for Yelp Users •Yelper Helper •Alex Ruiz-Euler •08/2014
- 3. Ye •Yelper Helper •PROBLEM •SOLUTION
- 4. •Yelper Helper: Overview. •Determine usefulness of new reviews •Compute user similarity •User making query
- 5. •Yelper Helper: Overview. •Determine usefulness of new reviews •Compute user similarity •User making query
- 7. •Review •User •Review attributes •User attributes •Business attributes •Usefu l tags •1 •Abe •Vocabulary richness, stars... •no. reviews, average rating... •Average rating... •3 •Predicting Number of “Useful” Tags •Data structure (Las Vegas): •363,691 reviews •112,702 users •3,536 businesses •(source: Yelp Academic Dataset)
- 8. •Validation: Poisson regression / Comparing AIC. •Feature Selection •Model Selection
- 9. •Yelper Helper: Overview. •Predict •usefulness of new reviews •Compute user similarity
- 10. •Yelper Helper: Overview. •Predict •usefulness of new reviews •Compute user similarity
- 11. •Use-taste matrix / Restaurant-category matrix •U: Ratings (stars) •Rest 1 •Rest 2 •Rest 3 •Rest 4 •User 1 •1 •3 •2 •User 2 •2 •4 •1 •User 3 •2 •1 •User 4 •1 •2 •1 •Hipster •Divey •Upscale •Intimate •Touristy •Classy •Romantic •Rest 1 •1 •1 •Rest 2 •1 •1 •Rest 3 •1 •1 •1 •Rest 4 •1 •1 •1 •V: Restaurant profile
- 12. •2 •User profile matrix •Hipster •Divey •Upscale •Intimate •Touristy •Classy •Romantic •User 1 •3 •1 •33 •1 •User 2 •2 •User 3 •1 •1 •1 •User 4 •3 •1•3 •2 •1 •3 •1 •5 •4 •4 •5 •2 •3 •1 •2 •3 •1 •3
- 13. •Similarity Matrix – Euclidean Distance Over UV. •User 1 •User 2 •User 3 •User 4 •User 1 •0 •User 2 •1.5 •0 •User 3 •2 •3.4 •0 •User 4 •7.2 •1 •2 •0
- 14. •About Me – Alex Ruiz-Euler (PhD Political Science, 2014)
- 15. •Thank You.
- 19. •Problem: ~75% of Yelp reviews have 0 “useful” tags*. •(* Las Vegas sample.)
- 20. •Issues with data •For similarity: Attributes of users in Yelp are about activity, not preferences. → Uncover taste preferences with collaborative filtering. •For prediction: Prediction of usefulness of review: a) Too many zeros (zero-inflated!). Weird results (null vs. full). → Zero-inflated Poisson model.