- Movie Recommendations with Spark Collaborative Filtering - Dec 1, 2021.
Not sure what movie to watch? Ask your recommender system.
Apache Spark, Collaborative, Knime, Low-Code, Recommender Systems
- Inside recommendations: how a recommender system recommends - Nov 17, 2021.
We describe types of recommender systems, more specifically, algorithms and methods for content-based systems, collaborative filtering, and hybrid systems.
Recommendation Engine, Recommender Systems
- Enhancing Machine Learning Personalization through Variety - Aug 19, 2021.
Personalization drives growth and is a touchstone of good customer experience. Personalization driven through machine learning can enable companies to improve this experience while improving ROI for marketing campaigns. However, challenges exist in these techniques for when personalization makes sense and how and when specific options are recommended.
Machine Learning, Personalization, Recommender Systems
- Similarity Search: Euclid of Alexandria goes shoe shopping - Jun 2, 2021.
Many applications can be improved with similarity search. Similarity search can provide more relevant results and therefore improve business outcomes such as conversion rates, engagement rates, detected threats, data quality, and customer satisfaction.
Neural Networks, Pinecone, Recommender Systems, Search
- 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
- Algorithms of Social Manipulation - Oct 9, 2020.
As we all continuously interact with each other and our favorite businesses through apps and websites, the level at which we are being tracked and monitored is significant. While the technologies behind these capabilities provide us value, the tech companies can also influence our decisions on where to click, spend our money, and much more.
Advertising, Algorithms, Amazon, Ethics, Google, Netflix, Recommender Systems, Search, Social Media, Uber
How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems - Oct 8, 2020.
LinkedIn uses some very innovative machine learning techniques to optimize candidate recommendations.
LinkedIn, Machine Learning, Recommendation Engine, Recommender Systems, Recruitment
- KDnuggets™ News 20:n32, Aug 19: The List of Top 10 Data Science Lists; Data Science MOOCs with Substance - Aug 19, 2020.
The List of Top 10 Lists in Data Science; Going Beyond Superficial: Data Science MOOCs with Substance; Introduction to Statistics for Data Science; Content-Based Recommendation System using Word Embeddings; How Natural Language Processing Is Changing Data Analytics
Courses, Data Analytics, Data Science, Data Science Skills, MOOC, NLP, Recommendation Engine, Recommender Systems, Statistics, Word Embeddings
- Content-Based Recommendation System using Word Embeddings - Aug 14, 2020.
This article explores how average Word2Vec and TF-IDF Word2Vec can be used to build a recommendation engine.
NLP, Recommendation Engine, Recommender Systems, TF-IDF, Word Embeddings, word2vec
- Building a Content-Based Book Recommendation Engine - Jul 28, 2020.
In this blog, we will see how we can build a simple content-based recommender system using Goodreads data.
Python, Recommendation Engine, Recommender Systems
- Recommender Systems in a Nutshell - Jul 23, 2020.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about recommender systems and the ways they are used.
Interview, Recommendation Engine, Recommender Systems
- Recommender System Metrics: Comparing Apples, Oranges and Bananas - Feb 11, 2020.
This article will discuss a sometimes-overlooked aspect of what distinguishes recommender systems from other machine learning tasks: added uncertainties of measuring them.
Metrics, Recommendation Engine, Recommender Systems
- Content-based Recommender Using Natural Language Processing (NLP) - Nov 26, 2019.
A guide to build a content-based movie recommender model based on NLP.
Movies, Netflix, NLP, Python, Recommender Systems
- Top KDnuggets tweets, Oct 16-22: How YouTube is Recommending Your Next Video - Oct 23, 2019.
Also: The 5 Classification Evaluation Metrics Every Data Scientist Must Know; How to Recognize a Good Data Scientist Job From a Bad One; How to Easily Deploy Machine Learning Models Using Flask.
Career, Flask, Metrics, Recommender Systems, Youtube
How YouTube is Recommending Your Next Video - Oct 21, 2019.
If you are interested in learning more about the latest Youtube recommendation algorithm paper, read this post for details on its approach and improvements.
Recommendation Engine, Recommender Systems, Video, Youtube
- An Easy Introduction to Machine Learning Recommender Systems - Sep 4, 2019.
Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.
Beginners, Machine Learning, Python, Recommendation Engine, Recommender Systems
- Top KDnuggets tweets, Aug 21-27: Algorithms Notes for Professionals – Free Book - Aug 28, 2019.
Algorithms Notes for Professionals - Free Book; 10 simple Linux tips which save 50% of my time in the command line; Why so many #DataScientists are leaving their jobs; Order Matters: Alibaba Transformer-based Recommender System
Algorithms, Recommender Systems, Top tweets, Unix
- Order Matters: Alibaba’s Transformer-based Recommender System - Aug 23, 2019.
Alibaba, the largest e-commerce platform in China, is a powerhouse not only when it comes to e-commerce, but also when it comes to recommender systems research. Their latest paper, Behaviour Sequence Transformer for E-commerce Recommendation in Alibaba, is yet another publication that pushes the state of the art in recommender systems.
Alibaba, Recommendation Engine, Recommender Systems, Transformer
How Data Science Is Used Within the Film Industry - Jul 5, 2019.
As Data Science is becoming pervasive across so many industries, Hollywood is certainly not being left behind. Learn about how Big Data, analytics, and AI are now core drivers of the movies we watch and how we watch them.
Data Science, Industry, Marketing, Movies, Predictive Analytics, Recommender Systems
- Building a Recommender System, Part 2 - Jul 3, 2019.
This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.
Movies, Python, Recommendation Engine, Recommender Systems
- Building Recommender systems with Azure Machine Learning service - May 15, 2019.
Microsoft has provided a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.
Azure ML, Machine Learning, Microsoft Azure, Recommender Systems
- KDnuggets™ News 19:n14, Apr 10: Which Data Science/ML methods and algorithms you used? Predict Age and Gender Using Neural Nets - Apr 10, 2019.
Getting started with NLP using the PyTorch framework; Building a Recommender System; Advice for New Data Scientists; All you need to know about text preprocessing for NLP and Machine Learning; Advanced Keras - Constructing Complex Custom Losses and Metrics; Top 8 Data Science Use Cases in Gaming
Career Advice, Convolutional Neural Networks, Courses, Data Preprocessing, Neural Networks, NLP, PyTorch, Recommender Systems
- Building a Recommender System - Apr 4, 2019.
A beginners guide to building a recommendation system, with a step-by-step guide on how to create a content-based filtering system to recommend movies for a user to watch.
Movies, Python, Recommendation Engine, Recommender Systems
- Pandora: Sr Scientist – Search, Information Retrieval & Recommendation Systems [Oakland, CA] - Mar 5, 2019.
Pandora is seeking a Scientist on the Personalization and Discovery team, where you’ll help change the way people listen. You will be at the center of the next generation audio service creation, utilizing advanced algorithms to bring a delightful and personalized listening experiences that is easy to access.
CA, Information Retrieval, Oakland, Pandora, Recommender Systems, Scientist
- Preparing for the Unexpected - Feb 28, 2019.
In some domains, new values appear all the time, so it's crucial to handle them in a good way. Using deep learning, one can learn a special Out-of-Vocabulary embedding for these new values. But how can you train this embedding to generalize well to any unseen value? We explain one of the methods employed at Taboola.
Data Preparation, Data Science, Overfitting, Recommender Systems
- Word Embeddings in NLP and its Applications - Feb 20, 2019.
Word embeddings such as Word2Vec is a key AI method that bridges the human understanding of language to that of a machine and is essential to solving many NLP problems. Here we discuss applications of Word2Vec to Survey responses, comment analysis, recommendation engines, and more.
Applications, NLP, Recommender Systems, Word Embeddings, word2vec
- State of the art in AI and Machine Learning – highlights of papers with code - Feb 20, 2019.
We introduce papers with code, the free and open resource of state-of-the-art Machine Learning papers, code and evaluation tables.
AI, Machine Learning, Multitask Learning, NLP, Papers with code, Recommender Systems, Semantic Segmentation, TensorFlow, Transfer Learning
- Wharton: Successful Applications of Customer Analytics – May 9-10, Philadelphia - Apr 18, 2018.
The WCAI annual conference, Successful Applications of Customer Analytics is dedicated to real-world applications that balance high-level rigor and business know-how, and to elevating the role of analytics in an organization strategic decision-making.
Applications, Customer Analytics, Deep Learning, PA, Philadelphia, Recommender Systems, WCAI, Wharton
- Wharton: Successful Applications of Customer Analytics, May 9-10, Philadelphia - Mar 28, 2018.
The WCAI annual conference, Successful Applications of Customer Analytics is dedicated to real-world applications that balance high-level rigor and business know-how, and to elevating the role of analytics in an organization strategic decision-making.
Applications, Customer Analytics, Deep Learning, PA, Philadelphia, Recommender Systems, WCAI, Wharton
- McKinsey Analytics Online Hackathon, 10 March, 2018 - Feb 28, 2018.
Calling all coders and data scientists to join McKinsey 24-hour hackathon on March 10, 2018. Win All-expenses paid trip to a tech conference of your choice.
Advanced Analytics, Hackathon, Machine Learning, McKinsey, Recommender Systems
- Recommender Engine - Under The Hood - Feb 21, 2018.
We examine two main types of recommender systems: Content based and Collaborative filtering. Both have their pros and cons depending upon the context in which you want to use them.
Recommendation Engine, Recommender Systems, TF-IDF
How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool - Oct 16, 2017.
In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users.
Deepak Agarwal, LinkedIn, Machine Learning, Recommendation, Recommender Systems
- Top KDnuggets tweets, Aug 23-29: Python overtakes R, becomes the leader in #DataScience, #MachineLearning; I built a #chatbot in 2 hours - Aug 30, 2017.
Also: Recommendation System Algorithms Overview; The Connection Between #DataScience, #MachineLearning and #AI; The Ultimate Guide to Basic Data Cleaning.
Chatbot, Neural Networks, Python vs R, Recommender Systems, Top tweets
- 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
- Top KDnuggets tweets, Aug 24-30: #DataScientist – sexiest job of the 21st century until …; Activation Function in #NeuralNetworks. - Aug 31, 2016.
Cartoon: #DataScientist - sexiest job of the 21st century until ...; What is the Role of the Activation Function in Neural Networks?; LinkedIn Machine Learning team tutorial on building #Recommender system; Create a #Chatbot for #Telegram in #Python to Summarize Text.
AI, LinkedIn, Mathematics, Neural Networks, Recommender Systems, Top tweets
- Top KDnuggets tweets, Apr 12-26: The Race For AI: Google, Facebook, Amazon, Apple; Comprehensive Guide to Learning #Python - Apr 27, 2016.
Data Science helps see where your country will stand in WW 3; Recommender Systems: New Comprehensive Textbook; Good read: Deep Learning in Neural Networks - extreme summary; The Race For #AI: Google, Facebook, Amazon, Apple rush to grab #AI startups.
AI, Charu Aggarwal, Deep Learning, Python, Recommender Systems, Top tweets
- KDnuggets™ News 16:n14, Apr 20: Top 15 Frameworks for Machine Learning Experts; How to Grow Your Own Data Scientists - Apr 20, 2016.
Top 15 Frameworks for Machine Learning Experts; How to Grow Your Own Data Scientists; Association Rules and the Apriori Algorithm: A Tutorial; Automated Machine Learning: Changing the Game.
Automated, Data Science Platform, Machine Learning, Recommender Systems
- Weekend Reading List: Free eBooks and Other Online Resources - Apr 16, 2016.
Do you have free time for reading this weekend? Here are a few new (or refreshed) selections of varying length for your leisure, along with a pair of papers, one cutting edge, and one classic.
Deep Learning, Free ebook, Recommender Systems, Resources
- Recommender Systems: New Comprehensive Textbook by Charu Aggarwal - Apr 15, 2016.
Covers recommender systems comprehensively, both fundamentals and advanced topics, organized into: Algorithms and evaluation, recommendations in specific domains and contexts, and advanced topics and applications.
Book, Charu Aggarwal, Recommender Systems
- Netflix Prize Analyzed: Movie Ratings and Recommender Systems - Mar 18, 2016.
A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems.
Free ebook, Netflix, Recommender Systems
- 21 Must-Know Data Science Interview Questions and Answers, part 2 - Feb 20, 2016.
Second part of the answers to 20 Questions to Detect Fake Data Scientists, including controlling overfitting, experimental design, tall and wide data, understanding the validity of statistics in the media, and more.
Pages: 1 2 3
Anomaly Detection, Data Science, Data Visualization, Overfitting, Recommender Systems
- 9 Must-Have Datasets for Investigating Recommender Systems - Feb 11, 2016.
Gain some insight into a variety of useful datasets for recommender systems, including data descriptions, appropriate uses, and some practical comparison.
Datasets, Lab41, Recommender Systems
- 5 Tribes of Machine Learning – Questions and Answers - Nov 27, 2015.
Leading researcher Pedro Domingos answers questions on 5 tribes of Machine Learning, Master Algorithm, No Free Lunch Theorem, Unsupervised Learning, Ensemble methods, 360-degree recommender, and more.
Ensemble Methods, Machine Learning, Pedro Domingos, Recommender Systems
- KDnuggets™ News 15:n36, Nov 4: Integrating R, Python; Neural Net in 11 lines; Top 20 AI/Machine Learning books - Nov 4, 2015.
Integrating Python and R; A Neural Network in 11 lines; Amazon Top 20 Books in AI, Machine Learning; How Big Data is used in Recommendation Systems to change to change our lives; Data Science of IoT.
AI, Books, Data Visualization, IoT, Machine Learning, Neural Networks, Recommender Systems, Topological Data Analysis
- 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
- 2015 RecSys Challenge: predicting online purchases from clicks - Nov 14, 2014.
ACM RecSys 2015 poses the 2015 RecSys Challenge, seeking researchers interested in prediction of purchases and clicks from online users using a dataset provided by a large European retailer.
Challenge, Customer Behavior, Recommender Systems, Retail
- UBS: Recommender System Specialist - Nov 4, 2014.
Analyze and interpret large amounts of client data in order to identify the specific needs of distinct client groups and to finally work up personalized investment proposals.
Recommender Systems, UBS, Zurich-Switzerland
- Top KDnuggets tweets, Aug 11-12: The entire global economy in one chart; New heroes of #BigData and Analytics - Aug 13, 2014.
The World Bank sums up the entire global economy in one chart; The new heroes of #BigData and Analytics - Chief Data Officers; Top 10 References for applying Big Data and Analytics in Business; Watch: "Building Recommender Systems" by Netflix Research/Eng Director.
Chief Data Officer, Economy, Netflix, Recommender Systems, World Bank
- 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
- Uni. Paderborn: Paid PhD position, Machine Learning / Predictive Analytics / Data Mining - May 27, 2014.
Support research projects in machine learning and data mining, help students in Kaggle competitions, help teach. Position is offered for 2 years with possible extension. This position is now closed.
Machine Learning, Paderborn-Germany, PhD position, Recommender Systems
- New Book on Realtime Analytics and Recommendation Engines - Jan 23, 2014.
The book covers realtime analytics and its application to recommendation engines from a control-theoretic perspective.
Realtime Analytics, Recommendation Engine, Recommender Systems
