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Recommender Systems: Offering the Right Product, to the Right Customer, At the Right Time.

As a business, if you can understand customers’ buying behaviors, you can sell them more products and services. One way to accomplish this is to sell your customers the products and services they’re interested in, at the exact time they’re thinking about a solution. This is where recommender systems come into play. A recommender system analyzes data to predict a user’s preference.

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Taysols: Next Best Offer Recommendation System using Amazon SageMaker
Learn more about This Is My Architecture at - https://amzn.to/2JJCIww. Recommendation systems are fundamental to today's digital experiences and helping customers find just the thing they need, not just want they want keeps customers coming back. But generating next best offers from data not just based on statistics, but sentiment. In this episode Taysols talks us through how they built a multi layered machine learning system using Amazon SageMaker to cluster similar customers behaviors, do better predictions through statistical analysis and generate compelling offers for customers. Host: Adrian De Luca, Partner Solutions Architect Customer: Dr Daniel Bassett, Chief Data Scientist Subscribe: More AWS videos http://bit.ly/2O3zS75 More AWS events videos http://bit.ly/316g9t4 #AWS

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Make Customer Experience the New Growth Driver with Next Best Action
Next Best Action looks at the best way of engaging with customers in the most relevant and efficient way using Machine Learning to automate, learn, and improve recommendations over time. In our explainer video, Datatonic's Senior Data Scientist, Pete Barty, covers: 00:08 What is Next Best Action 00:30 The Customer Analytics Roadmap: from manual Campaign Management to Next Best Action 2:20 Next Best Action in Practice: Maximising ROAS 4:05 The Four Foundational Layers of an effective NBA solution 6:42 Top Tips to Get Started

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Next - Best - Action Marketing Analytics
Learn how a Fortune 100 enterprise used automation for upselling and customer retention Key takeaways: How to scale your marketing efforts using analytics and automation Creating business rules that trigger upsell and customer retention opportunities ML-based recommendations for real-time execution With millions of customer interactions taking place each month, marketing teams often struggle to achieve customer intimacy. For effective upselling and customer retention, they need an automated approach to analyze and recommend next-best-actions from customer interactions. Impetus helped the marketing team of a Fortune 100 enterprise generate business rules that trigger customer engagement activities based on interaction patterns. Leveraging StreamAnalytix, we ingested data from multiple sources, transformed and filtered it, and applied business rules to generate customer retention and upsell actions. Join our upcoming webinar to learn how this approach can help transform your marketing efforts. Our experts will also highlight marketing analytics best practices and demonstrate how to set up and execute an interaction-based marketing solution.

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How to build a machine learning recommender systems and how to sell one to your boss
Collin Burton (MBA Class of '16) from BYU Analytics teaches what a recommender system is, how to build one, and how to sell the business value within an organization. Collin's LinkedIn profile - http://www.linkedin.com/in/collincburton Collin's Twitter - https://twitter.com/collincburton BYU Analytics Website - http://www.byuanalytics.com/ Follow BYU Analytics on Twitter - https://twitter.com/byuanalytics

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Building a Recommendation System in Python
===== Likes: 408 👍: Dislikes: 10 👎: 97.608% : Updated on 06-02-2022 10:41:25 EDT ===== Ever wonder how the recommendation algorithms work behind large tech companies? (Facebook, Google, Apple, Netflix, Amazon etc) Look no further! I explain how the recommendation systems work and how to create your own using Matrix Factorization and Kmeans clustering. I create a recommendation system for movies. So, stay tuned! ;) Github for code: https://github.com/SpencerPao/Data_Science/tree/main/Recommendation%20Systems Data Citation: F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligents Systems (TiiS) 5, 4: 19:1–19:19. Data Link: (MovieLens) https://grouplens.org/datasets/movielens/ 0:00 - Why do we care about Recommendation Algorithm & System? 1:22 - Game Plan! 1:38 - Collaborative Filtering and Content-Based Filtering & Objective 3:39 - Google Collab Setup & Data 7:18 - Matrix Factorization Model Initialization & Training / Tuning Model 10:30 - Kmeans Clustering & Movie Recommendations

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RecSys 2016: Paper Session 7 - Past, Present, & Future of Recommender Systems: Industry Perspective
Xavier Amatriain, Justin Basilico https://doi.org/10.1145/2959100.2959144 When the Netflix Prize launched in 2006, it put a spotlight on the importance and use of recommender systems in real-world applications. The competition provided many lessons, and many more have been learned since the Grand Prize was awarded in 2009. The use of recommender systems in industry has continued to grow driven by the availability of many kinds of user data and the continued interest for the area within the research community. In this paper, we will describe what we see as the past, present, and future of recommender systems from an industry perspective.

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What is Snowflake? 8 Minute Demo | Snowflake Inc.
See a brief overview of the Snowflake Cloud Data Platform in action. For a more in-depth demo, sign up for our weekly live demo program and have your questions answered by a Snowflake expert at https://bit.ly/2TdVCmJ. ❄Join our YouTube community❄ https://bit.ly/3lzfeeB Learn more about Snowflake: ➡️ Website: https://www.snowflake.com ➡️ Careers: http://careers.snowflake.com ➡️ Podcast page: https://bit.ly/3sFXst6 ➡️ Twitter: https://twitter.com/SnowflakeDB ➡️ Instagram: https://www.instagram.com/_snowflake_inc ➡️ Facebook: https://www.facebook.com/snowflakedb ➡️ LinkedIn: https://bit.ly/2QUexl4 Listen on: 🔈 Apple Podcasts: https://apple.co/3cCdrCU 🔈 Spotify: https://spoti.fi/39vCNjH 🔈 Simplecast: https://bit.ly/3rFCrgA #Snowflake #Data #DataCloud

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User - Based Collaborative Filtering
Full course: https://www.udemy.com/course/data-science-and-machine-learning-with-python-hands-on/?referralCode=C6B705087054C363CBEB One way to recommend items is to look for other people similar to you based on their behavior, and recommend stuff they liked that you haven't seen yet.

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Data Dimensions: Big data and next best action
http://www.capgemini.com/big-data-analytics Data Dimensions features leading Capgemini experts each adding their own dimension to the discussion around big data. Perspectives include defining big data, good governance in a big data world, finding the value behind the big data hype, and what building blocks organizations will need to set in place to make it work for them. It looks at the people aspects, the skills you need the move towards data driven decision making, digital transformation and the impact on the customer experience. Big data typically is very specific to industry so although there are common technologies and some common information sets ultimately each industry sector has many different new data sources and different business issues. Therefore a key part of this series is looking at how big data is affecting sectors and the associated opportunities it presents. Marketing has historically been about coarse groupings of 'prospects' split along relatively arbitrary lines. In an increasingly connected consumer centric world however the challenge for CMOs is to move away from coarse group marketing and towards focusing on the individuals and influencers that matter the most. Steve Jones talks about how Big Data and a new approach to Campaign Management helps CMOs treat customers as individuals in a chaotic world.

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Tutorial 1 - Weighted hybrid technique for Recommender system
Recommender system becomes very popular and has important role in an information system or webpages nowadays. A recommender system tries to make a prediction of which item a user may like based on his activity on the system. There are some familiar techniques to build a recommender system, such as content-based filtering and collaborative filtering github url: https://github.com/krishnaik06/Recommendation_complete_tutorial Below are the various playlist created on ML,Data Science and Deep Learning. Please subscribe and support the channel. Happy Learning! Deep Learning Playlist: https://www.youtube.com/watch?v=DKSZHN7jftI&list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi Data Science Projects playlist: https://www.youtube.com/watch?v=5Txi0nHIe0o&list=PLZoTAELRMXVNUcr7osiU7CCm8hcaqSzGw NLP playlist: https://www.youtube.com/watch?v=6ZVf1jnEKGI&list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm Statistics Playlist: https://www.youtube.com/watch?v=GGZfVeZs_v4&list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO Feature Engineering playlist: https://www.youtube.com/watch?v=NgoLMsaZ4HU&list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN Computer Vision playlist: https://www.youtube.com/watch?v=mT34_yu5pbg&list=PLZoTAELRMXVOIBRx0andphYJ7iakSg3Lk Data Science Interview Question playlist: https://www.youtube.com/watch?v=820Qr4BH0YM&list=PLZoTAELRMXVPkl7oRvzyNnyj1HS4wt2K- You can buy my book on Finance with Machine Learning and Deep Learning from the below url amazon url: https://www.amazon.in/Hands-Python-Finance-implementing-strategies/dp/1789346371/ref=sr_1_1?keywords=krish+naik&qid=1560943725&s=gateway&sr=8-1 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 THINGS to support my channel LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL

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Einstein Platform: Next Best Action
See how Salesforce admins and developers can use an easy drag-and-drop interface to deliver optimal recommendations at the point of maximum impact to employees or customers.

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Einstein Prediction Builder and Einstein Next Best Action
Einstein Prediction Builder and Einstein Next Best Action Einstein Prediction Builder Set Up and overview Einstein Next Best Action overview Einstein Next Best Action Set Up http://www.apexhours.com/einstein-prediction-builder-and-einstein-next-best-action/ #EinsteinPredictionBuilder #EinsteinNextBestAction

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What is RECOMMENDER SYSTEM? What does RECOMMENDER SYSTEM mean? RECOMMENDER SYSTEM meaning
✪✪✪✪✪ http://www.theaudiopedia.com ✪✪✪✪✪ What is RECOMMENDER SYSTEM? What does RECOMMENDER SYSTEM mean? RECOMMENDER SYSTEM meaning - RECOMMENDER SYSTEM definition - RECOMMENDER SYSTEM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. Recommender systems have become extremely common in recent years, and are utilized in a variety of areas: some popular applications include movies, music, news, books, research articles, search queries, social tags, and products in general. There are also recommender systems for experts, collaborators, jokes, restaurants, garments, financial services, life insurance, romantic partners (online dating), and Twitter pages. Recommender systems typically produce a list of recommendations in one of two ways – through collaborative and content-based filtering or the personality-based approach. Collaborative filtering approaches building a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties. These approaches are often combined (see Hybrid Recommender Systems). The differences between collaborative and content-based filtering can be demonstrated by comparing two popular music recommender systems – Last.fm and Pandora Radio. Last.fm creates a "station" of recommended songs by observing what bands and individual tracks the user has listened to on a regular basis and comparing those against the listening behavior of other users. Last.fm will play tracks that do not appear in the user's library, but are often played by other users with similar interests. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique. Pandora uses the properties of a song or artist (a subset of the 400 attributes provided by the Music Genome Project) in order to seed a "station" that plays music with similar properties. User feedback is used to refine the station's results, deemphasizing certain attributes when a user "dislikes" a particular song and emphasizing other attributes when a user "likes" a song. This is an example of a content-based approach. Each type of system has its own strengths and weaknesses. In the above example, Last.fm requires a large amount of information on a user in order to make accurate recommendations. This is an example of the cold start problem, and is common in collaborative filtering systems. While Pandora needs very little information to get started, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed). Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found by themselves. Interestingly enough, recommender systems are often implemented using search engines indexing non-traditional data. Montaner provided the first overview of recommender systems from an intelligent agent perspective. Adomavicius provided a new, alternate overview of recommender systems. Herlocker provides an additional overview of evaluation techniques for recommender systems, and Beel et al. discussed the problems of offline evaluations. Beel et al. have also provided literature surveys on available research paper recommender systems and existing challenges.

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Recommender Systems
This is CS50

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A simple python recommender

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Mini Project DSC651 Customer Churn in Bank Service

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Can You Predict Customer Churn ?
Predict customer churn using Python and Machine Learning. Please Subscribe ! ►Customer Churn Article: https://medium.com/@randerson112358/predict-customer-churn-using-python-machine-learning-b92f39685f4c ⭐Please Subscribe !⭐ ⭐Support the channel and/or get the code by becoming a supporter on Patreon: https://www.patreon.com/computerscience ⭐Websites: ► http://everythingcomputerscience.com/ ⭐Helpful Programming Books ► Python (Hands-Machine-Learning-Scikit-Learn-TensorFlow): https://amzn.to/2AD1axD ► Learning Python: https://amzn.to/3dQGrEB ►Head First Python: https://amzn.to/3fUxDiO ► C-Programming : https://amzn.to/2X0N6Wa ► Head First Java: https://amzn.to/2LxMlhT #Python #MachineLearning #AI #CustomerChurn

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How to do Churn Prediction of Customers? | Python Code Part - 1
This video is the Python Code Part - 1 of series and explains how to do Churn prediction of customers for a specific business' subscription service or w.r.t to a specific retails business. This is a two-part python hands-on series to showcase how to build Churn Prediction Machine Learning model from scratch ------------------------------------------------------------------------------------------------------------ SUPPORT ME on Patreon: https://www.patreon.com/theaiuniversity ------------------------------------------------------------------------------------------------------------ ******Links of Kindle & Machine Learning, Deep Learning & AI Books****** 1. Kindle 6" Display, 4GB, Wifi - https://amzn.to/2QJPqi7 2. Introduction to Machine Learning with Python - https://amzn.to/37UOmxM 3. Machine Learning: The Absolute Beginner’s Guide to Learn and Understand Machine Learning From Beginners, Intermediate, Advanced, To Expert Concepts - https://amzn.to/39YUVkI 4. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - https://amzn.to/35Nj5LI 5. Hands-On Machine Learning with Scikit-Learn and TensorFlow - https://amzn.to/2NhqAE6 6. Pattern Recognition and Machine Learning - https://amzn.to/2FFqSkj 7. Deep Learning with Python - https://amzn.to/36KCJsV 8. Deep Learning(Adaptive Computation and Machine Learning series) - https://amzn.to/30b90qR 9. Machine Learning: A Probabilistic Perspective - https://amzn.to/2FD1prH 10. Applied Artificial Intelligence - https://amzn.to/2QJyCI7 11. Life3.0 - https://amzn.to/2slfJ56 Exploratory Data Analysis in just 1 line of Python code - https://www.youtube.com/edit?o=U&video_id=D4pEev33c_Y&ar=1581488002026 Develop Dashboard for Business Intelligence & Data Science (Plotly Dash Tutorial Series) - https://www.youtube.com/playlist?list=PLlH6o4fAIji78Oa1ummuO_wtbLpWrJ9po One Hot Encoding to Process Categorical Variables - https://www.youtube.com/edit?o=U&video_id=tZ7KbIhYFDk&ar=1581489054073 FOLLOW ME ON: Twitter: https://twitter.com/theaiuniverse Facebook : https://www.facebook.com/theaiuniversity Instagram: https://www.instagram.com/theaiuniversity Telegram: https://t.me/theaiuniversity Tool for Keyword Research, Channel Health, Thumbnail Generation for your channel : https://www.Tubebuddy.com/theaiuniversity ▶ Check out my gear on Kit: https://kit.co/Nitin001 ▶ GITHUB REPO : https://github.com/nitinkaushik01/Deep_and_Machine_Learning_Projects ▶ SUBSCRIBE LINK: https://www.youtube.com/c/TheAIUniversity About this Channel: The AI University is a channel which is on a mission to democratize the Artificial Intelligence, Big Data Hadoop and Cloud Computing education to the entire world. The aim of this channel is to impart the knowledge to the data science, data analysis, data engineering and cloud architecture aspirants as well as providing advanced knowledge to the ones who already possess some of this knowledge. Please share, comment, like and subscribe if you liked this video. If you have any specific questions then you can comment on the comment section and I'll definitely try to get back to you. *******Other AI, ML, Deep Learning, Augmented Reality related Video Series***** Deploy Machine Learning Models as Web App using Flask & Docker on Azure Cloud - http://bit.ly/2Lgnd0g Machine Learning Data Pre-processing & Data Wrangling using Python - http://bit.ly/2K6psly Machine Learning & Deep Learning Project - http://bit.ly/2L0DUwz Machine Learning Projects in HINDI - http://bit.ly/2OSX7l5 Deep Learning Neural Network Tutorials - http://bit.ly/2K6e6hB Machine Learning & Deep Learning Bootcamp Series - http://bit.ly/2K4648Q Machine Learning using Spark MLLib - http://bit.ly/2QuKQGK Augmented Reality Free Tutorial - http://bit.ly/32ioysS Data Engineering Full Hands-on Course - http://bit.ly/2CsxSPz Hadoop, Machine & Deep Learning on Azure Cloud Tutorial Series - http://bit.ly/2K74l2I Natural Language Processing - http://bit.ly/2YtXQuF Develop Dashboard for Business Intelligence & Data Science(Plotly Dash Tutorial Series) - http://bit.ly/2Yu1Uen Data Science Tip and Tricks and Career Advice - http://bit.ly/2YvO6QE Machine Learning, Deep Learning Maths(Matrix & Vector Operations) - http://bit.ly/2YxhOEZ ****************************************************************** DISCLAIMER: This video and description may contain affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. #ChurnPrediction #MachineLearning #AttritionModel

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Recommendation Systems using Machine Learning
The most common types of recommendation systems are content based and collaborative filtering recommender systems. In collaborative filtering the behavior of a group of users is used to make recommendations to other users. Recommendation is based on the preference of other users. Recommendation implementation video url: https://www.youtube.com/watch?v=R64Lh1Qwl_0 Below are the various playlist created on ML,Data Science and Deep Learning. Please subscribe and support the channel. Happy Learning! Deep Learning Playlist: https://www.youtube.com/watch?v=DKSZHN7jftI&list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi Data Science Projects playlist: https://www.youtube.com/watch?v=5Txi0nHIe0o&list=PLZoTAELRMXVNUcr7osiU7CCm8hcaqSzGw NLP playlist: https://www.youtube.com/watch?v=6ZVf1jnEKGI&list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm Statistics Playlist: https://www.youtube.com/watch?v=GGZfVeZs_v4&list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO Feature Engineering playlist: https://www.youtube.com/watch?v=NgoLMsaZ4HU&list=PLZoTAELRMXVPwYGE2PXD3x0bfKnR0cJjN Computer Vision playlist: https://www.youtube.com/watch?v=mT34_yu5pbg&list=PLZoTAELRMXVOIBRx0andphYJ7iakSg3Lk Data Science Interview Question playlist: https://www.youtube.com/watch?v=820Qr4BH0YM&list=PLZoTAELRMXVPkl7oRvzyNnyj1HS4wt2K- You can buy my book on Finance with Machine Learning and Deep Learning from the below url amazon url: https://www.amazon.in/Hands-Python-Finance-implementing-strategies/dp/1789346371/ref=sr_1_1?keywords=krish+naik&qid=1560943725&s=gateway&sr=8-1 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 THINGS to support my channel LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL

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Making Movie Recommendations to People
Full course: https://www.udemy.com/course/building-recommender-systems-with-machine-learning-and-ai/?referralCode=88942B97679B7B050617 We'll implement a complete item-based collaborative filtering system that uses real-world movie ratings data to recommend movies to any user.

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