- Deep Learning Recommendation Models (DLRM): A Deep Dive - Apr 9, 2021.
The currency in the 21st century is no longer just data. It's the attention of people. This deep dive article presents the architecture and deployment issues experienced with the deep learning recommendation model, DLRM, which was open-sourced by Facebook in March 2019.
Deep Learning, Recommendations, Recommender Systems
- Recommendation Engines and Real-time personalization – download guidebook - Oct 26, 2017.
Recommendation engines are effective because they expose users to content they may not have otherwise found. For a step-by-step guide on building an effective recommendation engine from the ground up, check out our latest guidebook.
Dataiku, Free ebook, Real-time, Recommendation Engine, Recommendations
- Recommendation System Algorithms: An Overview - Aug 22, 2017.
This post presents an overview of the main existing recommendation system algorithms, in order for data scientists to choose the best one according a business’s limitations and requirements.
Algorithms, Recommendations, Recommender Systems, Statsbot
- Does Machine Learning allow opposites to attract? - Feb 11, 2016.
Most online dating sites use 'Netflix-style' recommendations which match people based on their shared interests and likes. What about those matches that work so well because people are so different - here is my example.
Love, Machine Learning, Online Dating, Recommendations
- More Data Science Humor and Cartoons - Dec 23, 2015.
More humor and cartoons from Andrii aka San Sanych, #HappyDataScientist.
Cartoon, Humor, Recommendations
- Cartoon: Surprise Data Science Recommendations - Dec 19, 2015.
We revisit KDnuggets cartoon which examines an unexpected shopping recommendation from Big Data and machine learning algorithms.
Cartoon, Python, Recommendations
- Advance your career in DATA SCIENCE with Divergence Academy - Nov 9, 2015.
Divergence Academy has multiple Big Data, Data Science, and Machine learning programs geared for the working professional, those in transition or student with programming skills, and help you get placed in DFW or another area.
Apache Spark, Dallas, Data Science Education, Ft. Worth, Python, Recommendations, TX
- How Data Science increased the profitability of the e-commerce industry? - Nov 3, 2015.
Data Science helps businesses provide a richer understanding of the customers by capturing and integrating the information on customers web behaviour, their life events, what led to the purchase of a product or service, how customers interact with different channels, and more.
Pages: 1 2
Data Science, DeZyre, Ecommerce, Recommendations
- How Big Data is used in Recommendation Systems to change our lives - Oct 30, 2015.
A Recommendation systems have impacted or even redefined our lives in many ways. It works in well-defined, logical phases which are data collection, ratings, and filtering.
Pages: 1 2 3
Amazon, Big Data, Kaushik Pal, Recommendations, Recommender Systems
- Disney: Decisioning Development Manager II - Oct 1, 2015.
Responsible for the in-house management of Guest decisioning in WDPR real-time personalization tool; engage the business to frame, structure and prioritize business decisioning needs, and meet those needs through the creation, and coding, of content decisioning logic resulting in tailored recommendations.
Analytics Manager, Celebration, Decision Management, Disney, FL, Personalization, Recommendations
- Interview: Thanigai Vellore, Art.com on Why Big Data vs RDBMS is the Wrong Question - Jul 24, 2015.
We discuss success factors with polyglot architectures, Big Data challenges, recommendations for using Big Data technologies, trends, advice, and more.
Architecture, Art.com, Big Data, Career, Challenges, Hadoop, Interview, RDBMS, Recommendations
- HP Big Data Helps Ford to Better Manage Fleets and Personalize Employee Drives - Jul 23, 2015.
HP-Ford partnership is leveraging Big Data for the next level of Telematics insights based intelligence.
Coffee, Experimentation, Ford, HP, Optimization, Recommendations, Telematics, Vertica
- Interview: Reiner Kappenberger, HP Security Voltage on Security Checklist for Data Architectures - Jul 10, 2015.
We discuss securing data-at-rest and data-in-motion, security recommendations for data architectures, trends, advice, and more.
Architecture, HP, HP Security Voltage, Interview, Recommendations, Reiner Kappenberger, Security, Trends
- Interview: Emmanuel Letouzé, Data-Pop Alliance on Democratizing the Benefits of Big Data - Apr 24, 2015.
We discuss the 3 Cs of Big Data, state of ethics in the field of Big Data, and how to ensure that the benefits of Big Data reach the masses.
Pages: 1 2
Data Democratization, Data-Pop Alliance, Emmanuel Letouze, Interview, MIT Media Lab, Recommendations
- Interview: Michael Li, Data Incubator on Bridging the Data Science Skills Gap between Academia and Industry - Apr 21, 2015.
We discuss the response from hiring companies, recommendations for aspirants, retaining data science talent, advice, and more.
Academics, Advice, Career, Data Science Skills, Industry, Interview, Machine Learning, Recommendations, Trends
- Interview: Xia Wang, AstraZeneca on Unraveling Patient Treatment Journey by NLP on Clinical Notes - Apr 9, 2015.
We discuss Analytics at AstraZeneca, prominent use cases, how NLP helped understanding patient treatment journey in diabetes, data sources, insights, and more.
AstraZeneca, Healthcare, Insights, NLP, Recommendations, Research, Xia Wang
- Interview: Brad Klingenberg, StitchFix on Building Analytics-powered Personal Stylist - Mar 20, 2015.
We discuss StitchFix, how it leverages Analytics, understanding customer preferences, and pros-and-cons of involving human judgement in the recommendation process.
Analytics, Brad Klingenberg, Customer Experience, Recommendations, Stitch Fix
- Ontotext Webinar: Semantic Publishing, Enhancing Content and Engagement, Mar 26 - Mar 17, 2015.
Ontotext will show how news and media publishers can use semantic publishing technology to more efficiently generate content while increasing audience engagement through personalization and recommendations.
Ontotext, Personalization, Recommendations, Semantic Analysis
- Prismatic Interest Graph [API]: Organize and Recommend Content - Feb 20, 2015.
Prismatic Interest Graph API provides a set of tools for automatically analyzing unstructured text and annotating it with a variety of tags that are useful for organizing and recommending content.
Machine Learning, Prismatic, Recommendations, Text Analytics, Text Mining
- Interview: Jason Bloomberg, Intellyx on the Tricky Balance of Optimization and Innovation - Feb 6, 2015.
We discuss Agile Digital Transformation, Optimization vs Innovation trade-off, best innovations of 2014, trends, advice and more.
Data-Driven Business, Disruptive, Innovation, Intellyx, Jason Bloomberg, Optimization, Recommendations
- Comics Recommendations: “Tinder for Comics” built with Tapastic and PredictionIO - Feb 2, 2015.
Here is how we built a cool demo of recommending comics, using PredictionIO new Similar Product Template and dataset provided by Tapastic.com.
Cartoon, PredictionIO, Recommendations, Tapastic, Tinder
- GoodData Insights as a Service guides users thru the analytics process - Jan 27, 2015.
GoodData launches Insights Network that goes beyond BI and gives uses recommendations that guide them through the analytics process. I ask them about it.
Big Data Services, Cloud Analytics, GoodData, Recommendations
- Top KDnuggets tweets, Dec 17-18: Why Amazon Ratings Might Mislead You; Open Source Tools for Machine Learning - Dec 19, 2014.
Why #Amazon Ratings Might Mislead You: The Story of Herding Effects; Open Source Tools for Machine Learning; #DeepLearning Intelligence Platform - Addressing AML #Terrorism #Financing; #NIPS2014 #MachineLearning Trends: Rapid progress in #DeepLearning.
Amazon, Deep Learning, NIPS, Open Source, Recommendations, Terrorism
- Interview: Peter Alvaro, UC Berkeley, on Managing Asynchrony and Partial Failure - Dec 18, 2014.
We discuss the challenges in simultaneously managing asynchrony and partial failure, the problem of composition, research motivation, trends and more.
Challenges, Cloud, Peter Alvaro, Programming Languages, Recommendations, Trends, UC Berkeley
- Top KDnuggets tweets, Dec 15-16: KDnuggets Cartoon: Unexpected Machine Learning Recommendations - Dec 17, 2014.
KDnuggets Cartoon: Unexpected Machine Learning Recommendations; Review: #DataScience at the Command Line - great book; Most Demanded Data Science and Data Mining Skills; The problem is that many ML researchers are not working on on most impactful areas.
Book, Cartoon, Data Science, Data Science Skills, Jeremy Howard, Recommendations
- Cartoon: Unexpected Data Science Recommendations - Dec 16, 2014.
New KDnuggets cartoon examines an unexpected shopping recommendation from Big Data and machine learning algorithms.
Cartoon, Machine Learning, Python, Recommendations
- Customer Analytics Summit 2014 Chicago: Day 1 Highlights - Sep 11, 2014.
Highlights from the presentations by Big Data & Analytics experts from ShareThis, Netflix and Ancestry on day 1 of Customer Analytics Summit 2014.
Chicago-IL, Conference, Customer Analytics, IE Group, Netflix, Recommendations, ShareThis
- Interview: Debora Donato, StumbleUpon on the Secret Sauce of Impressive Content Curation - Aug 28, 2014.
We discuss the role of data science at StumbleUpon, the shift from search to discovery, metrics for user engagement, the art of collaborative filtering, how native ads improve user experience, major trends, advice and more.
Advertising, Advice, Customer Engagement, Data Curation, Debora Donato, Interview, Recommendations, StumbleUpon, Trends, User Experience
- Interview: Saikat Mukherjee, ShareThis on Why Marketers can no longer Ignore Social TV? - Aug 20, 2014.
We discuss the role of Analytics at ShareThis, the emergence of Social TV, better user behavior insights through Social TV, major challenges with Social TV analytics, interesting insights, future trends, recommendation and more.
Challenges, Consumer Insights, Marketing, Prediction, Recommendations, Saikat Mukherjee, ShareThis, Social Media, Social TV
- Top stories for Jul 27 – Aug 2: What does Big Data tell us about Romance - Aug 3, 2014.
Interview: Thomas Levi, PlentyOfFish on what does Big Data tell us about Romance and how online dating is improving matching through Big Data; Predictive Analytics Innovation Summit 2014 London: Day 1 Highlights; Probabilistic Approaches to Recommendations.
London-UK, Online Dating, Predictive Analytics, Recommendations, Thomas Levi, Top stories
- Book: Probabilistic Approaches to Recommendations - Jul 28, 2014.
Learn about the challenges of the recommendation problem and common probabilistic solutions to it, then dive into state of the art techniques in Probabilistic Approaches to Recommendation.
Book, Morgan & Claypool, Recommendations, Recommender Systems
- Interview: Kirk Borne, Data Scientist, GMU on Big Data in Astrophysics and Correlation vs. Causality - May 30, 2014.
We discuss how to build the best data models, significance of correlation and causality in Predictive Analytics, and impact of Big Data on Astrophysics.
Correlation, Interview, Kirk D. Borne, Predictive Analytics, Recommendations
- NineSigma Big Data Analytics RFP - May 9, 2014.
NineSigma is seeking proposals for mining user browsing/operations history, social networking services, and sensing devices to improve personalization and recommendation of products. Submit by May 23, 2014.
NineSigma, Personalization, Proposals, Recommendations, RFP
- White House Report on Big Data: Opportunities and Values - May 9, 2014.
We summarize the key findings in the recently released White House report on Big Data, highlight the key opportunities and concerns, and list the recommendations made to the President.
Big Data Privacy, Government, Recommendations, Report, White House
- Top KDnuggets tweets, Mar 26-27: Watch “Statistics with R for newbies”; Coursera free #DataScience courses - Mar 28, 2014.
Also free ebooks on Practical Machine Learning: Innovations in Recommendations, and Apache Hive - How to access big data on Hadoop with SQL/HiveQL.
Apache Hive, Coursera, Hadoop, Machine Learning, newbies, R, Recommendations, Statistics
- Swiss Analytics Magazine launched - Mar 28, 2014.
The very first issue of the Swiss Analytics Magazine is now available online. The primary objective of this publication is to provide original analytics content to Swiss practitioners.
Analytics, Magazine, Recommendations, SAA, Sandro Saitta, Switzerland
