TikTok Like Recommender And AWS Personalize

• https://medium.com/data-science-collective/1-building-a-tiktok-like-recommender-a64563262c1a is the source for the TikTok-like build, where a two-tower model feeds a four-stage pipeline for H&M fashion items. Item embeddings can be precomputed, so a query costs one embedding lookup plus a nearest neighbour search. The custom route keeps full control of the model.
• https://blog.cloudmentor.pro/blog/aws-mla/solution-personalize-customer is the source for the AWS case, where Amazon Personalize serves a Kansai energy group and Bedrock writes ad copy from product images. The flow there runs on managed services only, from Amplify and API Gateway to Kinesis and SageMaker, and it reported revenue growth of nearly 230%.

Building a TikTok-Like Recommender

Introduction

The article shows how to build a real-time personalized recommender for H&M fashion items, using a 4-stage architecture and a two-tower model. The goal is a system that can handle millions of items and deliver relevant recommendations to users.

Two-Tower Model

The two-tower model is a deep learning architecture made of two neural networks trained in parallel:

Query/Customer Encoder

• Converts customer features into a dense embedding vector
• Handles several kinds of features:
  • Demographics: Age, gender, location, etc.
  • Historical behavior: Purchase history, views, past interactions
  • Contextual features: Time, device, geographic location

Item Encoder

• Converts item features into embedding vectors in the same vector space as the customer embeddings
• Handles product features:
  • Tags: Type, category, brand
  • Description: Detailed product information
  • Reviews: Ratings and user comments

Benefits of the two-tower model

• Efficient at scale: Item embeddings can be precomputed and stored in a database or an approximate nearest neighbor (ANN) search index
• Fast queries: The customer embedding only needs to be computed once, then similar items are searched in the vector space
• Personalization: Each customer has their own embedding vector reflecting their preferences and behavior

4-Stage Recommender Architecture

The recommender is split into 4 stages to optimize performance and accuracy:

Stage 1: Candidate Generation

• Purpose: Process a large set of items and retrieve a relevant subset for the later ranking and filtering steps
• Method: Use the two-tower model to find similar items in the vector space
• Scale: From millions of items down to hundreds or thousands of candidates

Stage 2: Filtering

• Purpose: Apply filters to remove unneeded items before ranking
• Types of filters:
  • Business filters: Remove out-of-stock products and those that violate policy
  • User filters: Remove products the user has already bought or recently viewed
  • Diversity filters: Ensure the recommendation list is varied in product type

Stage 3: Ranking

• Purpose: Assign a score to each "candidate item, customer" pair based on relevance
• Method: Use a more complex ranking model to compute accurate scores
• Input: The customer embedding vector and the item embedding vector
• Output: A relevance score

Stage 4: Re-ranking

• Purpose: Order the items based on the ranking score and other business logic
• Influencing factors:
  • The score from the ranking model
  • Business logic (prioritize new and best-selling products)
  • Diversification (avoid too many products of the same type)
  • Balance between exploration and exploitation

Applying It to the H&M Case

Data

• Uses the "H&M Personalized Fashion Recommendations" dataset
• Includes information on:
  • Customers: Demographics, shopping behavior
  • Items: Product information, category, images
  • Transactions: Purchase history, views, interactions

Model

• Applies the two-tower model to create embeddings for customers and items
• Trains the model on historical data to learn interaction patterns

Deployment

• Uses the 4-stage architecture to deliver real-time personalized recommendations
• Optimized to handle millions of items and millions of users

Conclusion

The TikTok-like recommender uses a two-tower model and a 4-stage architecture to deliver effective personalized recommendations at scale. This architecture lets the system handle millions of items and provide relevant recommendations for each user in real time.

Building a Recommender with Generative AI for Marketing

Customer Story

The largest electricity, gas, and home appliance business group in Kansai, Japan, wanted to build an intelligent recommendation system similar to e-commerce platforms such as TikTok, Shopee, and Amazon.

System requirements

• Real-time recommendations: Recommend in real time to improve the user experience
• Support for unregistered users: Users without an account can also use it
• No infrastructure concerns: The customer doesn't want to worry about or manage application infrastructure and source code
• Business focus: Focus only on sales strategy
• Scalability: The system can scale up and down flexibly during big sale events and new product releases

System Overview

Infrastructure

• Platform: Deployed on AWS
• Architecture: Multi-region, multi-AZ (Multi-Availability Zone)
• Management: Uses ControlTower to manage enterprise accounts

Front-end

• Technology: ReactJS to build the web/app UI
• Integration: Amazon Amplify to manage authentication, hosting, and other front-end services

Back-end

• Technology: Java Spring for the API logic
• Containerization: Build images and push them to ECR (Elastic Container Registry)
• Deployment: Deploy with ECS Fargate (serverless container platform)
• Auto-scaling: Auto scaling based on CPU metrics, end to end

Data/Machine Learning

• Recommendation Engine: AWS Personalize
• Data Processing: AWS DataBrew (visual data preparation tool)

Machine Learning Solution Workflow (5 Steps)

Step 1: Gathering data (data collection)

The system splits data into 3 main datasets:

UserEvent/Interactions (user interaction data)

• Role: Extremely important for sales, especially in e-commerce
• Content: Collects user interaction data:
  • Product views
  • Product clicks
  • Product purchases
  • Add to cart
  • Other interaction behaviors
• Collection flow:
  1. A JavaScript "Click Stream Events" library on the web/app collects the data
  2. Data is sent up through API Gateway
  3. Fed into Kinesis Data Streams (real-time streaming)
  4. Kinesis Data Firehose (batch processing and delivery)
  5. Finally stored on S3

Item metadata (product metadata)

• Content: Detailed product information:
  • Product name
  • Description
  • Price
  • Category
  • Images
  • Other attributes
• Source: Exported from the RDS database as a CSV file

User metadata

• Content: Users' personal information:
  • Name
  • Age
  • Email
  • Location
  • Other demographic information
• Source: Exported from the RDS database as a CSV file

Step 2: Data pre-processing

AWS DataBrew

• Description: A visual data preparation tool, no code required
• Features: More than 250 pre-built transformations
• Functions:
  • Perform ETL (Extract, Transform, Load) on the data
  • Remove empty values
  • Remove duplicates
  • Reformat data (convert types: string -> int, long, etc.)
• Benefit: Quick and lightweight, no Python code needed

Analysis and reporting

• Analyze the data and build charts from the figures
• Export PDFs and share the data with stakeholders for review

Data storage

• Location: Datasets are stored multi-region on AWS S3
• Security:
  • S3 versioning enabled (keeps versions of files)
  • KMS (Key Management Service) encrypts the data following AWS best practices
• Cost optimization: S3 lifecycle integrated to save cost (moves to cheaper storage classes after some time)

Step 3: Model building and training with AWS Personalize

After datasets are imported into Personalize, Amazon Personalize provides recipes, which are pre-designed algorithms for solving user use cases.

The system uses 4 built-in algorithms, corresponding to 4 models for the different use cases:

USER_PERSONALIZATION

• Purpose: Recommend products/services to a user from the product catalog
• Application: Used on the homepage to personalize the user experience
• Example: When a user logs in for the 2nd or 3rd time, the homepage prioritizes products that match the user's preferences based on their previous interaction history
• Algorithm: HRNN (Hierarchical Recurrent Neural Network)
  • Designed to process time-ordered user history data
  • Uses Recurrent Neural Networks (RNNs)
  • Predicts user behavior from the preceding sequence of interactions

RELATED_ITEM

• Purpose: Suggest products similar to the one being viewed
• Application: Applied on the detail page (product detail page)
• Example: If a customer often buys electric stoves, the system recommends new electric stoves, gas stoves, and other similar items the user hasn't viewed
• Algorithm: Cosine Similarity
  • A popular method for measuring similarity between two vectors
  • Vectors typically represent items based on their features
  • Cosine Similarity is a value from -1 (completely dissimilar) to 1 (completely similar), with 0 indicating independence

PERSONALIZED_RANKING

• Purpose: Rank the list of recommended products for a user when they search for any product
• Basis: Based on ratings/interactions
• Example: When a user searches for an electric stove, the system ranks the search results by how well they match the user's preferences and behavior

USER_SEGMENTATION

• Purpose: Segment customers based on their characteristics
• Basis for segmentation:
  • Demographic characteristics
  • Purchasing behavior
  • Customer psychology
• Algorithm: kNN (k-Nearest Neighbors)
• Application: Used in marketing strategy to target the right audience

Step 4: Model Evaluation and Deployment

Model evaluation

The system uses 2 evaluation methods:

Part 1: Automatic evaluation with metrics

• F1 Score: Reached the maximum (1.000)
• Meaning: The model achieved the maximum for both precision and recall (1.000)
• Precision: Accuracy of the recommendations (the share of correct recommendations)
• Recall: Coverage (the share of relevant products that were recommended)

Part 2: Evaluation with a test case suite

• More than 300 test cases defined by the customer
• Manual test runs (run by hand) to ensure quality

Deploy model

• Platform: After the model is deployed on an AWS SageMaker Endpoint
• Access: Users/applications can invoke the API endpoint to get results
• Architecture:
  • Client -> API Gateway
  • Lambda is used to invoke the SageMaker Endpoint API
  • Returns the recommendation results to the client

Step 5: Monitor Model

• Logging: Logs are stored on CloudWatch
• Visualization: Logs are sent from CloudWatch to Grafana hosted on EC2
• Dashboard: A dashboard tracks:
  • Model performance metrics
  • API request volume
  • Other key metrics

Generative AI Integration

After the Generative AI boom of 2023-2024, demand for using GenAI in the advertising and marketing industry has grown steadily.

Goal

Automatically generate ad copy based on:

• Product images from the database
• Product/service information
• The target audience of the advertising campaign (already processed and collected in USER_SEGMENTATION - customer segmentation)

GenAI integration flow

Step 1: The user provides input data

• Product image: Choose the product image to advertise; the images are stored in S3
• Ad service type: Choose the type of service:
  • Mail Marketing
  • SMS
  • Web Content
  • Post SNS (Social Network Service)
  • Others
• Target audience: Choose a user group segmented by Amazon Personalize

Step 2: Data handling through AWS AppSync

• AWS AppSync: Makes it easy to build GraphQL APIs without managing infrastructure
• WebSocket Subscription: The user's input is sent to the system through AWS AppSync over a websocket subscription connection
• Real-time Updates: Lets results update in real time as the data is processed

Step 3: Image processing with Amazon Rekognition

• Amazon Rekognition: AWS's service for analyzing objects, scenes, and context in images
• Analysis functions:
  • Image labels: Labels identifying the objects and scenes in the image
  • Dominant colors: The dominant colors in the image
• Purpose: Helps the system understand the content of the image (e.g., the image has flowers, a jogger, food, etc.) and use that information to create accurate and meaningful ad content

Step 4: Generate ad copy with Amazon Bedrock

• Amazon Bedrock: AWS's platform for running AI language models
• LLM Model: Uses Titan Image Generator G1 v2
• Flow:
  1. Receive the image analysis data from Rekognition
  2. Combine it with the product/service information
  3. Combine it with the target audience
  4. Build a prompt for the language model (LLM)
  5. Amazon Bedrock generates the ad content automatically from the prompt

Step 5: Publish the result

• Send to the user: The generated ad content is sent back to the user through AWS AppSync
• Storage: Save the content in the campaign system for advertising

Extended feature: Switch Models

• Concept: Freely switch between leading models from OpenAI, IBM, AWS, etc.
• Principle: The higher the model's price, the better the quality of its output
• Benefits:
  • Each model was trained on its own data
  • Each model has its own way of "expressing itself"
  • Groundbreaking and interesting
  • Flexible in choosing the model that fits each use case

Benefits and Effectiveness of the GenAI System

Process automation

• Before: Text-only content
• Now: The system generates ad content that includes:
  • Images
  • Text
  • Matching colors
• Result: More accurate and relevant ad content

Cost and time optimization

Traditional process

• People required:
  • Designer
  • Copywriter
  • Marketing team
• Time: At least 1 week for the stages of:
  • Ideation
  • Design
  • Development
  • Review
• Result: 1 PR post/video about a new product

Process with GenAI

• Time: Only about 1 day
• Result: Can produce as many as 100 product PR posts/videos
• Note: Use more, pay more
• Benefits:
  • If something isn't suitable, it can be downloaded and customized
  • No fear of copyright infringement

Cost comparison

• Traditional cost: Paying a designer/copywriter/marketing team certainly costs more than paying AWS
• GenAI cost: Pay only for the AWS services used
• Benefit: If a campaign has hundreds of products, release time is much faster while still "ensuring quality" (if modern, state-of-the-art models are used)

Revenue growth

After applying ML solutions to:

• Product recommendation
• User segmentation strategy for the marketing market

Result: Revenue grew nearly 230% over the previous year.

Conclusion

A solution that builds an intelligent product recommender and integrates generative AI into advertising and marketing brings many benefits:

1. Personalized experience: The system can recommend in real time, including for unregistered users
2. Automation: Reduces manual work and speeds up content development
3. Cost optimization: Reduces labor cost and development time
4. Revenue growth: Significant growth thanks to personalization and automation
5. Scalability: The system can scale up and down flexibly with demand

This is a comprehensive solution, from data collection and processing to model building and generative AI integration, aimed at improving the user experience and optimizing business strategy.

Comparing the Two Approaches

Similarities

• Both use deep learning models to create embeddings for users and products
• Both apply a multi-stage architecture to optimize performance
• Both aim to personalize the user experience

Differences

When to use which approach?

TikTok-like Recommender (Custom)

• When you need full control over the model and algorithms
• When you have a professional ML team
• When you need deep customization of the model architecture
• When you want to optimize cost at very large scale (after the initial investment)

AWS Personalize with GenAI

• When you want to focus on business logic rather than infrastructure
• When you need to deploy quickly
• When you want built-in integration with other AWS services
• When you need GenAI features to create marketing content
• When you want to minimize infrastructure and scaling risk

See also: Recommender Systems · AI Powered AB Testing With Amazon Bedrock · KNN K Nearest Neighbors