Forem: Khai Bui The latest articles on Forem by Khai Bui (@khaibq). https://forem.com/khaibq https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F2934199%2F945e5d36-4f15-4b72-bb31-b0b4944d11a9.jpg Forem: Khai Bui https://forem.com/khaibq en Hands-On Practice: Amazon Personalize Khai Bui Tue, 20 Jan 2026 13:15:03 +0000 https://forem.com/khaibq/hands-on-practice-amazon-personalize-3kol https://forem.com/khaibq/hands-on-practice-amazon-personalize-3kol <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F16ui0v8xk3zc0lybrveg.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F16ui0v8xk3zc0lybrveg.png" alt="Amazon Personalize" width="400" height="400"></a></p> <h2> Brief Overview </h2> <p><strong>Amazon Personalize</strong> enables businesses to deliver relevant, real-time recommendations <strong>without ML expertise</strong>. It supports diverse use cases from product suggestions to content discovery, driving revenue growth and customer satisfaction across e-commerce, media, travel, finance, education, and gaming industries.</p> <h3> Key Industries &amp; Applications </h3> <h4> E-Commerce </h4> <ul> <li>Product recommendations</li> <li>Personalized homepage content</li> <li>Cart add-on suggestions</li> <li>Search result re-ranking</li> </ul> <h4> Media &amp; Entertainment </h4> <ul> <li>Video/movie recommendations</li> <li>Personalized music playlists</li> <li>Content discovery</li> <li>"Continue watching" suggestions</li> </ul> <h4> News &amp; Publishing </h4> <ul> <li>Personalized article feeds</li> <li>Related story suggestions</li> <li>Custom newsletter content</li> </ul> <h4> Travel &amp; Hospitality </h4> <ul> <li>Destination recommendations</li> <li>Hotel and flight suggestions</li> <li>Personalized activity packages</li> </ul> <h4> Retail &amp; Fashion </h4> <ul> <li>Style and outfit recommendations</li> <li>"Complete the look" suggestions</li> <li>Reorder reminders</li> </ul> <h4> Financial Services </h4> <ul> <li>Product recommendations (cards, loans)</li> <li>Personalized investment suggestions</li> <li>Targeted offers</li> </ul> <h4> Education </h4> <ul> <li>Course recommendations</li> <li>Personalized learning paths</li> <li>Skill-based content matching</li> </ul> <h4> Gaming </h4> <ul> <li>Game recommendations</li> <li>In-game item suggestions</li> <li>Player matching</li> </ul> <h2> Available Recipes </h2> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>Recipe</th> <th>Purpose</th> </tr> </thead> <tbody> <tr> <td>USER_PERSONALIZATION</td> <td>Personalization per user. Items by purchases, views. Recommended for you. Popularity count. Most popular.</td> </tr> <tr> <td>USER_SEGMENTATION</td> <td>Item and attribute affinity.</td> </tr> <tr> <td>PERSONALIZED_ACTIONS</td> <td>Best action.</td> </tr> <tr> <td>PERSONALIZED_RANKING</td> <td>Re-rank search results for user.</td> </tr> <tr> <td>RELATED_ITEMS</td> <td>Customers who viewed x also-viewed. Frequently bought together. Similar items.</td> </tr> <tr> <td>TRENDING_NOW</td> <td>Currently trending content.</td> </tr> </tbody> </table></div> <h2> Common Event Types </h2> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>Industry</th> <th>Events</th> </tr> </thead> <tbody> <tr> <td>E-commerce</td> <td>view, click, add_to_cart, purchase</td> </tr> <tr> <td>Streaming</td> <td>play, pause, complete, like</td> </tr> <tr> <td>News</td> <td>read, share, bookmark</td> </tr> <tr> <td>Travel</td> <td>search, book, favorite</td> </tr> </tbody> </table></div> <h2> Business Benefits </h2> <ul> <li>10-30% increase in conversions</li> <li>Higher engagement and click-through rates</li> <li>Improved retention and reduced churn</li> <li>Automated personalization at scale</li> </ul> <h2> Hands-On Practice </h2> <p>This guide will walk you through building a recommendation system by <strong>AWS CDK in Python</strong> with 02 stacks:</p> <ul> <li>Build the infra such as dataset created in S3, configuring Dataset Group, Schema and Recipes (<a href="https://clean-equipment.com/docs/category/hands-on-practice-amazon-personalize---part-i" rel="noopener noreferrer"><strong>Part I</strong></a>).</li> <li>Create the Pipeline stack to orchestra the automatic workflow (<a href="https://clean-equipment.com/docs/category/hands-on-practice-amazon-personalize---part-ii" rel="noopener noreferrer"><strong>Part II</strong></a>).</li> <li>Upload dataset, execute the State machine to create the solution (model training) and campaign (<a href="https://clean-equipment.com/docs/category/hands-on-practice-amazon-personalize---part-iii" rel="noopener noreferrer"><strong>Part III</strong></a>).</li> </ul> aws communitybuilder