Increase Customer Engagement and Conversion Rates by providing highly personalized experiences across digital channels through recommendations, curated content, and targeted marketing promotions.Â
Push relevant recommendations to your customers, based on their online behavior, profile, purchasing patterns, location, time, and more. Improve customer engagement and conversio
Understand the Use-case under 5 minutes
Video (3 minutes)
Improve customer engagement and conversion, and differentiate your business by creating personalized web experiences tailored to individual customer preferences
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Video (5 minutes)
A gentle introduction expanding on how Personalized Recommendations work and featuring industry examples from Amazon, BestBuy, Netflix, and YouTube
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Article (20 minutes)
A holistic article covering many business and technical aspects related to Personalized Recommendations: ROI, Success Factors, Role of AI, Implementation methodologies, and more
Read
Article (4 minutes)
Expands on different forms of Personalized Recommendations, why AI-based ones are different from traditional methods, 4+ case studies, and how to choose a good solution
VisitCase studies, Organizational Aspects, Return on Investment examples
Article (17 minutes)
Tips and Specific KPIs for measuring the Effectiveness of different phases of your Personalized Recommendations efforts: Impact, Interaction, and Conversation
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Articles (5 minutes/Case)
Different case studies with ROI examples for organizations using Machine Learning to Personalize their Online Experience for customers
Read
Article (12 minutes)
Some tech and organizational challenges commonly faced when implementing Personalization: Data Collection, Organizational Silos, Complete Customer Profile, and more
VisitMore details on the technical aspects of the use-case
Article (8 minutes)
There are many different algorithms for building a recommender system and various types of filtering.This article touched on item-based collaborative filtering and user-based collaborative filtering.
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Video (40 minutes)
AWS Personalize is a code-less AI service that you can use to personalize your website experience for users instead of building a recommendation engine from scratch. Video shows tech details, scenarios, and a demo
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Articles (20 minutes)
Details about How AWS Personalize Works, Setting it up, Preparing and Importing your Data, Creating a Solution/Campaign, and Getting Recommendations
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Video (6 minutes)
High level explanation for common recommender systems techniques: Collaborative Filtering, Content based Filtering, and Hybrid Recommendation Systems
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo
This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks: Data Prep, Model Training, Optimization, & Deployment
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Article (50 minutes)
Covers various types of recommendation engines, mathematics behind the algorithms, and building a recommendation engine from scratch using matrix factorization
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Videos
High level explanation for common recommender systems techniques: Collaborative Filtering, Content based Filtering, and Hybrid Recommendation Systems
VisitGithub Repo
Notebooks and examples on how to onboard and use various features of Amazon Personalize (Cloud AI Service)
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Videos
High level explanation for common recommender systems techniques: Collaborative Filtering, Content based Filtering, and Hybrid Recommendation Systems
Visit
Data Sets you can use to build Demos, POCs, or test Algorithms
A collection of datasets that have been collected for research by UCSD’s lab. Datasets contain the following features: user/item interactions, star ratings, timestamps, & more. Data coming from: Amazon, Pinterest, GoodReads, Facebook, Twitter, Reddit, Behance, & more
Off-the-Shelf Products using AI for Personalized Recommendations
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