Yelper Helper Concept
•Download as PPT, PDF•
0 likes•193 views
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.
Report
Share
Report
Share
Recommended
Yelper Helper Concept
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.
School Of 2020 (Nicolas & Salvador)
The document discusses potential future technologies in schools, including audio recorders for students to record lessons, digital notebooks to replace physical ones, and individual screens at student desks rather than projectors for whole classes. It also outlines current technologies used in classrooms like projectors, computers, printers, and smart boards, as well as security cameras and ID doors used outside classrooms. The trends toward more digital tools and personalized devices for students are expected to continue into the future.
Rct Pi M Consistory Presentation 060810
Presentation of capital projects proposal to board of directors of historic New York State Dutch Reformed Church.
Data for action research
The document provides information on various English and math assessment tests available through OnDemand, including adaptive tests measuring different skills levels and linear tests aligned to VELs levels, and describes the Principles of Learning and Teaching and a classroom observation where students discussed their preferred math activities like tangrams and working on laptops.
Redes sociales
Este documento explora las redes sociales en el contexto educativo. Pregunta si las redes sociales son una moda pasajera o tendrán un papel importante en la sociedad, y si la escuela debería enseñar a los estudiantes sobre sus oportunidades y amenazas. También considera si las escuelas deberían tener presencia en redes como Facebook o Twitter. Define las redes sociales como estructuras sociales que conectan grupos de personas a través de relaciones como la amistad. Finalmente, destaca usos educativos potenciales
Library
A library is a place for silent reading where books are your friends. It is a place to gain information and enjoy reading. Library rules require books be returned within 2 weeks or fines will be charged, and a large fine will be charged if a book is lost or damaged. Reading books is entertaining, improves knowledge and vocabulary, and allows learning new things.
TecnologíA De InformacióN Y ComunicacióN
Lección numero uno del curso "uso del portal" Proyecto FINEANS y Universidad de San Carlos de Guatemala.
Yelper Helper Concept
The document describes Yelper Helper, a personalized review engine for Yelp users. It aims to predict the usefulness of new reviews and compute user similarity. It does this by using attributes of reviews, users and businesses to build models. These include using a zero-inflated Poisson distribution to predict the number of "useful" tags for a review since 75% have 0 tags. It also computes user similarity through collaborative filtering on user ratings of restaurants to uncover taste preferences. Issues addressed are that Yelp user attributes reflect activity, not preferences, and the high number of zero "useful" tags requires a zero-inflated model for accurate prediction.
Recommended
Yelper Helper Concept
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.
School Of 2020 (Nicolas & Salvador)
The document discusses potential future technologies in schools, including audio recorders for students to record lessons, digital notebooks to replace physical ones, and individual screens at student desks rather than projectors for whole classes. It also outlines current technologies used in classrooms like projectors, computers, printers, and smart boards, as well as security cameras and ID doors used outside classrooms. The trends toward more digital tools and personalized devices for students are expected to continue into the future.
Rct Pi M Consistory Presentation 060810
Presentation of capital projects proposal to board of directors of historic New York State Dutch Reformed Church.
Data for action research
The document provides information on various English and math assessment tests available through OnDemand, including adaptive tests measuring different skills levels and linear tests aligned to VELs levels, and describes the Principles of Learning and Teaching and a classroom observation where students discussed their preferred math activities like tangrams and working on laptops.
Redes sociales
Este documento explora las redes sociales en el contexto educativo. Pregunta si las redes sociales son una moda pasajera o tendrán un papel importante en la sociedad, y si la escuela debería enseñar a los estudiantes sobre sus oportunidades y amenazas. También considera si las escuelas deberían tener presencia en redes como Facebook o Twitter. Define las redes sociales como estructuras sociales que conectan grupos de personas a través de relaciones como la amistad. Finalmente, destaca usos educativos potenciales
Library
A library is a place for silent reading where books are your friends. It is a place to gain information and enjoy reading. Library rules require books be returned within 2 weeks or fines will be charged, and a large fine will be charged if a book is lost or damaged. Reading books is entertaining, improves knowledge and vocabulary, and allows learning new things.
TecnologíA De InformacióN Y ComunicacióN
Lección numero uno del curso "uso del portal" Proyecto FINEANS y Universidad de San Carlos de Guatemala.
Yelper Helper Concept
The document describes Yelper Helper, a personalized review engine for Yelp users. It aims to predict the usefulness of new reviews and compute user similarity. It does this by using attributes of reviews, users and businesses to build models. These include using a zero-inflated Poisson distribution to predict the number of "useful" tags for a review since 75% have 0 tags. It also computes user similarity through collaborative filtering on user ratings of restaurants to uncover taste preferences. Issues addressed are that Yelp user attributes reflect activity, not preferences, and the high number of zero "useful" tags requires a zero-inflated model for accurate prediction.
Yelper Helper Concept
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.
PredictingYelpReviews
This document summarizes a study that developed a model to predict the number of "useful" votes a Yelp review will receive. It finds that an extreme gradient boosting model (xgbtree2) using only 5 key features can accurately predict review usefulness, with a root-mean-squared error of 0.41-0.49 depending on the business category. The study analyzes Yelp data on over 500,000 restaurant reviews to validate the model, finding it tends to slightly underestimate reviews receiving larger numbers of useful votes. To improve predictions, the model is trained and applied separately to each of 10 major business categories.
Yelp Rating Prediction
This document summarizes research on predicting Yelp ratings for restaurants from user reviews. The author collected review and business data from Yelp and divided it into development, cross-validation, and test sets. Various classifiers including Naive Bayes, SVM, and logistic regression were tested on the cross-validation data, with logistic regression performing best. Feature engineering using POS tags and selecting the top 500 features improved results. Tuning logistic regression with L1 regularization further optimized performance. The author concludes POS features and selecting an optimal number of top features improves predictive accuracy and discusses ideas for future work.
A Supervised Modeling Approach to Determine Elite Status of Yelp Members
The document describes research conducted by a team of students at Carnegie Mellon University to predict whether a user on the Yelp platform will obtain elite status. The team analyzed attributes of users in the Yelp Academic Dataset to identify correlations with elite status. They found that number of reviews, number of fans, and counts of useful, cool, and funny votes showed significant differences between elite and non-elite users. The team then experimented with various machine learning algorithms like decision trees, k-nearest neighbors, and linear regression to classify users and predict elite status. Their best model achieved a prediction accuracy of 94.2% using a decision tree algorithm.
Yelp's Review Filtering Algorithm Paper
In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews were most likely to be recommended when conveying an overall positive message written in a few moderately complex sentences expressing substantive detail with an informative range of varied sentiment. Other factors relating to patterns and frequency of platform use also bear strongly on review recommendations. Though not without important ethical implications, the findings are logically consistent with Yelp’s efforts to facilitate, inform, and empower consumer decisions.
Crimson Publishers-Toward Generating Customized Rehabilitation Plan and Deliv...
Crimson Publishers-Toward Generating Customized Rehabilitation Plan and Deliv...Crimsonpublishers-Rehabilitation
Toward Generating Customized Rehabilitation Plan and Delivering to Engaged Patient by Weijia LU in Examines in Physical Medicine & RehabilitationCollaborative Filtering 1: User-based CF
This document discusses recommender systems and collaborative filtering. It introduces user-based collaborative filtering, which predicts a user's rating for an item based on the ratings from similar users. Similarity between users is calculated using the Pearson correlation coefficient. The ratings of the top K most similar users are then averaged to predict the target user's rating.
Rating System Algorithms Document
Here everything is cleared out but unfortunately we couldn't accomplish our main objective i.e. to make a new algorithm from existing ones but our approach has reached much far that one can understand and contribute to this project
Predicting Yelp Review Star Ratings with Language
This document describes research conducted to predict star ratings for Yelp reviews using language feature analysis. The researchers used linear regression models to analyze basic readily available features from the Yelp dataset, including business star ratings, user average ratings, and review vote counts. Topic models using latent Dirichlet allocation (LDA) were also analyzed as advanced language features extracted from review texts. Stemming text prior to LDA improved predictive performance compared to the baseline model. The best performing model used a combination of basic features and LDA topic distributions, reducing mean squared error over the baseline.
Exploratory data analysis and data mining on yelp restaurant review
The document discusses exploratory data analysis (EDA) performed on a Yelp restaurant review dataset to gain insights. Key analysis included identifying the top restaurant categories and filtering to the most reviewed category of restaurants. Spatial and temporal analysis of KFC reviews found Pennsylvania had the highest ratings and reviews have been declining since 2014. Bag-of-words modeling was used to identify the most frequent words and phrases in positive ("good food"), neutral ("good not great"), and negative ("bad customer service") reviews. The EDA provided useful insights about popular restaurants and how sentiment has changed over time.
Recommendation Systems with R
Recommendation systems use collaborative filtering to find similarities between users or items. They analyze large datasets to build rating matrices representing users' ratings of items. User-based collaborative filtering finds similar users to a target user and uses their ratings to predict the target user's ratings, while item-based collaborative filtering directly recommends similar items to those the target user liked. Both approaches aim to predict users' preferences to make personalized recommendations.
2014-User Modeling for Contextual Suggestion-TREC
This paper describes a user modeling approach for contextual suggestion. The key aspects are:
1) It builds separate user interest models to represent a user's general interests in categories and specific interests in content words, using a reinforcement learning algorithm called RAMA.
2) It retrieves candidate suggestions from the Yelp website API based on location context.
3) It calculates scores for each candidate based on the user models and a context model, then ranks candidates by combining the scores, submitting two runs with different weightings.
FinalInnovationWeek-Dec12
The document discusses an algorithmic team that estimates property values and collects user feedback. It extracts property data to estimate prices and allows users to provide feedback. The team's goals are to check feedback for mistakes, easily review feedback, and potentially discover new features or automatically adjust estimates based on feedback over time. Feedback is currently sorted based on detail, accuracy, source, and normalization. Geographic and text clustering analysis of feedback are also discussed.
amazonalexa-191018052738 (5).pdf
This dataset consists of nearly 3000 Amazon customer reviews of Alexa products like the Echo, Echo Dot, and Alexa Show. It includes variables for star ratings, date of review, product variant, whether the review is verified, and sentiment. The objectives are to analyze the reviews to discover insights, train machine learning models for sentiment analysis, and analyze the relationships between variables. Sentiment analysis will be used to classify reviews as positive or negative. Plots and summaries will analyze patterns in the data, like most reviews being 5 stars. Linear regression will also be used to establish relationships between variables like ratings and sentiment.
Amazon alexa ppt
This dataset consists of a nearly 3000 Amazon customer reviews (input text), and 5 variables which are star ratings, date of review, variant, verified reviews and feedback of various Amazon Alexa products like Alexa Echo, Echo dots, Alexa Firesticks etc.
Yelp Fake Reviews Detection_new_v23.pptx
This document summarizes a project to identify fake reviews on the Yelp dataset for New York City restaurants. It describes the dataset containing over 350,000 reviews labeled as true or fake. Preprocessing steps included merging datasets, handling missing values, text processing to remove stopwords and stemming words. Behavioral and text features were extracted, including sentiment scores, review length, number of capital words. Classification methods like logistic regression, naive Bayes, and KNN were applied and results were presented. References for related work detecting fake reviews on Yelp were also provided.
Yelp an Assessment of Customer Satisfaction
A marketing research report on Yelp that I completed for my market research class. Prepare for graphs and numbers!
Yelp Presentation
Yelp is a social media platform where users can write crowd-sourced reviews about local businesses. It started in 2004 and allows both individuals and small businesses to participate. Yelp has grown significantly over time and now receives over 135 million unique visitors per month. It provides APIs and open data sets to allow researchers to analyze user reviews and behaviors. Common areas of academic research involve recommendation systems, sentiment analysis, and understanding trends using the large amount of data available on Yelp.
Text Data Mining and Predictive Modeling of Online Reviews
This document describes a study that uses text mining of online restaurant reviews to build a model to predict a restaurant's rating on Yelp. Researchers scraped over 96,000 reviews of 300 Mexican restaurants from major US cities to use as training and testing data. After processing the text, they used the Google Prediction API to build a model based on word frequencies. Their goal was to provide a tool for restaurants to estimate potential future Yelp ratings based on early customer feedback to help with business decisions. Evaluation of the model on held-out test data showed it had reasonably predictive power to serve this purpose.
Innovation week dec12
The document discusses how a valuations team extracts property data like number of beds and baths to estimate property prices using algorithms. Users can provide feedback on the estimates. The team's goals are to check for obvious mistakes in feedback, facilitate easy review of feedbacks, and potentially discover new useful features from feedback. Feedback is sorted based on detail, accuracy, source, and normalization. Text clustering is used to analyze feedback text and discover frequently occurring terms.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
More Related Content
Similar to Yelper Helper Concept
Yelper Helper Concept
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.
PredictingYelpReviews
This document summarizes a study that developed a model to predict the number of "useful" votes a Yelp review will receive. It finds that an extreme gradient boosting model (xgbtree2) using only 5 key features can accurately predict review usefulness, with a root-mean-squared error of 0.41-0.49 depending on the business category. The study analyzes Yelp data on over 500,000 restaurant reviews to validate the model, finding it tends to slightly underestimate reviews receiving larger numbers of useful votes. To improve predictions, the model is trained and applied separately to each of 10 major business categories.
Yelp Rating Prediction
This document summarizes research on predicting Yelp ratings for restaurants from user reviews. The author collected review and business data from Yelp and divided it into development, cross-validation, and test sets. Various classifiers including Naive Bayes, SVM, and logistic regression were tested on the cross-validation data, with logistic regression performing best. Feature engineering using POS tags and selecting the top 500 features improved results. Tuning logistic regression with L1 regularization further optimized performance. The author concludes POS features and selecting an optimal number of top features improves predictive accuracy and discusses ideas for future work.
A Supervised Modeling Approach to Determine Elite Status of Yelp Members
The document describes research conducted by a team of students at Carnegie Mellon University to predict whether a user on the Yelp platform will obtain elite status. The team analyzed attributes of users in the Yelp Academic Dataset to identify correlations with elite status. They found that number of reviews, number of fans, and counts of useful, cool, and funny votes showed significant differences between elite and non-elite users. The team then experimented with various machine learning algorithms like decision trees, k-nearest neighbors, and linear regression to classify users and predict elite status. Their best model achieved a prediction accuracy of 94.2% using a decision tree algorithm.
Yelp's Review Filtering Algorithm Paper
In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews were most likely to be recommended when conveying an overall positive message written in a few moderately complex sentences expressing substantive detail with an informative range of varied sentiment. Other factors relating to patterns and frequency of platform use also bear strongly on review recommendations. Though not without important ethical implications, the findings are logically consistent with Yelp’s efforts to facilitate, inform, and empower consumer decisions.
Crimson Publishers-Toward Generating Customized Rehabilitation Plan and Deliv...
Crimson Publishers-Toward Generating Customized Rehabilitation Plan and Deliv...Crimsonpublishers-Rehabilitation
Toward Generating Customized Rehabilitation Plan and Delivering to Engaged Patient by Weijia LU in Examines in Physical Medicine & RehabilitationCollaborative Filtering 1: User-based CF
This document discusses recommender systems and collaborative filtering. It introduces user-based collaborative filtering, which predicts a user's rating for an item based on the ratings from similar users. Similarity between users is calculated using the Pearson correlation coefficient. The ratings of the top K most similar users are then averaged to predict the target user's rating.
Rating System Algorithms Document
Here everything is cleared out but unfortunately we couldn't accomplish our main objective i.e. to make a new algorithm from existing ones but our approach has reached much far that one can understand and contribute to this project
Predicting Yelp Review Star Ratings with Language
This document describes research conducted to predict star ratings for Yelp reviews using language feature analysis. The researchers used linear regression models to analyze basic readily available features from the Yelp dataset, including business star ratings, user average ratings, and review vote counts. Topic models using latent Dirichlet allocation (LDA) were also analyzed as advanced language features extracted from review texts. Stemming text prior to LDA improved predictive performance compared to the baseline model. The best performing model used a combination of basic features and LDA topic distributions, reducing mean squared error over the baseline.
Exploratory data analysis and data mining on yelp restaurant review
The document discusses exploratory data analysis (EDA) performed on a Yelp restaurant review dataset to gain insights. Key analysis included identifying the top restaurant categories and filtering to the most reviewed category of restaurants. Spatial and temporal analysis of KFC reviews found Pennsylvania had the highest ratings and reviews have been declining since 2014. Bag-of-words modeling was used to identify the most frequent words and phrases in positive ("good food"), neutral ("good not great"), and negative ("bad customer service") reviews. The EDA provided useful insights about popular restaurants and how sentiment has changed over time.
Recommendation Systems with R
Recommendation systems use collaborative filtering to find similarities between users or items. They analyze large datasets to build rating matrices representing users' ratings of items. User-based collaborative filtering finds similar users to a target user and uses their ratings to predict the target user's ratings, while item-based collaborative filtering directly recommends similar items to those the target user liked. Both approaches aim to predict users' preferences to make personalized recommendations.
2014-User Modeling for Contextual Suggestion-TREC
This paper describes a user modeling approach for contextual suggestion. The key aspects are:
1) It builds separate user interest models to represent a user's general interests in categories and specific interests in content words, using a reinforcement learning algorithm called RAMA.
2) It retrieves candidate suggestions from the Yelp website API based on location context.
3) It calculates scores for each candidate based on the user models and a context model, then ranks candidates by combining the scores, submitting two runs with different weightings.
FinalInnovationWeek-Dec12
The document discusses an algorithmic team that estimates property values and collects user feedback. It extracts property data to estimate prices and allows users to provide feedback. The team's goals are to check feedback for mistakes, easily review feedback, and potentially discover new features or automatically adjust estimates based on feedback over time. Feedback is currently sorted based on detail, accuracy, source, and normalization. Geographic and text clustering analysis of feedback are also discussed.
amazonalexa-191018052738 (5).pdf
This dataset consists of nearly 3000 Amazon customer reviews of Alexa products like the Echo, Echo Dot, and Alexa Show. It includes variables for star ratings, date of review, product variant, whether the review is verified, and sentiment. The objectives are to analyze the reviews to discover insights, train machine learning models for sentiment analysis, and analyze the relationships between variables. Sentiment analysis will be used to classify reviews as positive or negative. Plots and summaries will analyze patterns in the data, like most reviews being 5 stars. Linear regression will also be used to establish relationships between variables like ratings and sentiment.
Amazon alexa ppt
This dataset consists of a nearly 3000 Amazon customer reviews (input text), and 5 variables which are star ratings, date of review, variant, verified reviews and feedback of various Amazon Alexa products like Alexa Echo, Echo dots, Alexa Firesticks etc.
Yelp Fake Reviews Detection_new_v23.pptx
This document summarizes a project to identify fake reviews on the Yelp dataset for New York City restaurants. It describes the dataset containing over 350,000 reviews labeled as true or fake. Preprocessing steps included merging datasets, handling missing values, text processing to remove stopwords and stemming words. Behavioral and text features were extracted, including sentiment scores, review length, number of capital words. Classification methods like logistic regression, naive Bayes, and KNN were applied and results were presented. References for related work detecting fake reviews on Yelp were also provided.
Yelp an Assessment of Customer Satisfaction
A marketing research report on Yelp that I completed for my market research class. Prepare for graphs and numbers!
Yelp Presentation
Yelp is a social media platform where users can write crowd-sourced reviews about local businesses. It started in 2004 and allows both individuals and small businesses to participate. Yelp has grown significantly over time and now receives over 135 million unique visitors per month. It provides APIs and open data sets to allow researchers to analyze user reviews and behaviors. Common areas of academic research involve recommendation systems, sentiment analysis, and understanding trends using the large amount of data available on Yelp.
Text Data Mining and Predictive Modeling of Online Reviews
This document describes a study that uses text mining of online restaurant reviews to build a model to predict a restaurant's rating on Yelp. Researchers scraped over 96,000 reviews of 300 Mexican restaurants from major US cities to use as training and testing data. After processing the text, they used the Google Prediction API to build a model based on word frequencies. Their goal was to provide a tool for restaurants to estimate potential future Yelp ratings based on early customer feedback to help with business decisions. Evaluation of the model on held-out test data showed it had reasonably predictive power to serve this purpose.
Innovation week dec12
The document discusses how a valuations team extracts property data like number of beds and baths to estimate property prices using algorithms. Users can provide feedback on the estimates. The team's goals are to check for obvious mistakes in feedback, facilitate easy review of feedbacks, and potentially discover new useful features from feedback. Feedback is sorted based on detail, accuracy, source, and normalization. Text clustering is used to analyze feedback text and discover frequently occurring terms.
Similar to Yelper Helper Concept (20)
A Supervised Modeling Approach to Determine Elite Status of Yelp Members
A Supervised Modeling Approach to Determine Elite Status of Yelp Members
Crimson Publishers-Toward Generating Customized Rehabilitation Plan and Deliv...
Crimson Publishers-Toward Generating Customized Rehabilitation Plan and Deliv...
Exploratory data analysis and data mining on yelp restaurant review
Exploratory data analysis and data mining on yelp restaurant review
Text Data Mining and Predictive Modeling of Online Reviews
Text Data Mining and Predictive Modeling of Online Reviews
Recently uploaded
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
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
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
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.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Operating System Used by Users in day-to-day life.pptx
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Trusted Execution Environment for Decentralized Process Mining
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Finale of the Year: Apply for Next One!
Presentation for the event called "Finale of the Year: Apply for Next One!" organized by GDSC PJATK
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
This case study explores designing a scalable e-commerce platform, covering key requirements, system components, and best practices.
Recently uploaded (20)
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Operating System Used by Users in day-to-day life.pptx
Operating System Used by Users in day-to-day life.pptx
Trusted Execution Environment for Decentralized Process Mining
Trusted Execution Environment for Decentralized Process Mining
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Yelper Helper Concept
- 1. Personalized Review Engine for Yelp Users Yelper Helper Alex Ruiz- Euler 08/2014
- 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. Revie w Use r Review attribute s User attributes Business attribute s Usefu l tags 1 Abe Vocabular y 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 Hipste r Dive y Upscal e Intimat e Tourist y Classy Romanti c 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 Dive y Upscale Intimate Tourist y Classy Romanti c User 1 3 1 33 1 User 2 2 User 3 1 1 1 User 4 3 13 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.