• Table of Contents {toc}

Overview

  • look at Learning Strategy in concepts

  • about 10 mins of values

  • Each round below will also have questions about values
    • Snap Core Values
    • Kind - We listen from the heart, think empathetically, and help each other grow.
    • Smart - We think deeply, question conventions, and strive to never stop learning.
    • Creative - We challenge the status quo to make things with a sense of purpose.

Aman

L’Interview

  • 1h coding: Leetcode hard? easy? any particular data structures or algorithms, Graphs —> communicate while coding, trade offs, leetcode medium, Graphs/Trees questions
  • 1h ML fundamentals: Resume deep dive, expertise in recsys, large scale projects, cross functional, this type of work.. Skill fit. All fair game on resume. NLP experts will ask.
  • 1hr applied ML/ML design : building out a recommendation system, modeling component, ambiguous question, facebook friend recommendation. Ask clarifying questions, get a sense of design. Google draw
  • 1h system design: -> engineering side, system that the model sits in. Infrastructure focused, machine learning type question, infra side
    • Need to figure it out
  • 1h product-focused: -> competitive analysis, real time surface recommendation, corgi instantly, local model federated learning can update faster Yarun product focused, are you interested in whats going on with other companies. TikTok and reels familiar and interested in, cross collaborate, research per compnay
    • related papers and research analysis
  • 1h leadership / Q&A : Jun, people leadership, challenging conversations, cross functionally, promote others, priorritizing team (have values in mind, clarifying questions) concise communication, logical

Management

  • First let’s start with Management frameworks

Debugging ML Models

  • Check your data: Start by examining your data to ensure its quality and correctness. Look for missing values, outliers, or inconsistent data formats. Data preprocessing steps like data cleaning, normalization, and feature engineering can also introduce errors, so validate those steps.
  • Review your model architecture: Verify that your model architecture is appropriate for your problem. Check if the model is too complex, leading to overfitting, or too simple, resulting in underfitting. You can try simpler models, regularization techniques, or adjusting hyperparameters to improve performance.
  • Inspect your model’s predictions: Examine the predictions your model is making to identify patterns and potential issues. Compare predicted outputs with the ground truth values. Look for systematic errors, such as consistently overpredicting or underpredicting certain classes or regions of the input space
  • Evaluate performance metrics: Assess various performance metrics, such as accuracy, precision, recall, or F1 score, depending on your problem type. These metrics can give you insights into the strengths and weaknesses of your model. Consider using validation and test datasets to assess performance and avoid overfitting.
  • Visualize model behavior: Visualizations can help you understand what your model is learning and how it’s making predictions. For example, you can visualize feature importances, decision boundaries, or activation maps in convolutional neural networks. Visualizations can reveal issues like data leakage, biased predictions, or mislabeled data.
  • Isolate and reproduce issues: If you’re encountering specific issues or errors, try to isolate and reproduce them. Simplify your inputs or modify your code to create a minimal example that highlights the problem. This can help you understand the root cause and develop potential solutions.
  • Debug incrementally: Rather than trying to tackle all potential issues at once, narrow down the possibilities. Gradually debug and validate each component of your machine learning pipeline, such as data preprocessing, model architecture, loss function, or optimization algorithm.
  • Check assumptions: Make sure you’re not violating any assumptions underlying your machine learning algorithm. For instance, linear models may assume linearity, and tree-based models assume feature independence. Violating these assumptions can lead to poor performance.
  • Cross-validation and ensemble methods: Cross-validation can provide a more robust estimate of your model’s performance. It helps identify issues related to data distribution, overfitting, or generalization. Ensemble methods, such as bagging or boosting, can also help improve model performance and reduce overfitting.
  • Follow strategies here

Managing high and low performer

Managing High and Low Performers:

1) Managing a Senior Leader Struggling with Adaptability: Handling a senior leader who has a track record for great products but struggles with adaptability and being receptive to change can be challenging. Here are some strategies to address this situation:

  • Open Communication: Initiate open and honest conversations with the senior leader to understand their concerns, perspective, and resistance towards change. Create a safe space for them to express their opinions and provide feedback.

  • Highlight the Benefits: Clearly communicate the benefits of adopting newer, more optimized tools like ANN (Artificial Neural Networks) over less optimized ones like KNN (K-Nearest Neighbors). Explain how using more advanced tools can lead to improved performance, efficiency, and competitive advantage.

  • Demonstrate Success Stories: Share success stories and case studies of other teams or organizations that have successfully implemented the recommended changes. Show how these changes have positively impacted their outcomes, productivity, and innovation.

  • Provide Training and Support: Offer training sessions, workshops, or learning resources to help the senior leader understand the advantages of the new tools and concepts. Provide them with opportunities to gain hands-on experience and build their confidence in using these tools effectively.

  • Collaborative Feedback Process: Establish a feedback loop where the senior leader receives constructive feedback from colleagues, including the CR (Change Request) team. Encourage a culture of continuous improvement and learning, emphasizing that feedback is essential for personal and professional growth.

  • Recognize and Leverage Expertise: Acknowledge the senior leader’s expertise and contributions in their areas of strength. Leverage their knowledge and experience to mentor and guide other team members, fostering a sense of value and purpose.

2) Managing an Intern Turned Full-Time Employee Struggling with Time Management: When an intern who has been converted to a full-time employee is facing challenges with time management and prioritization, it is crucial to provide guidance and support to help them succeed. Consider the following approaches:

  • Mentorship and Guidance: Assign a mentor to the employee who can provide guidance on effective time management techniques, prioritization strategies, and help them navigate their responsibilities. The mentor can offer insights, share personal experiences, and provide regular feedback to help the employee improve.

  • Goal Setting and Prioritization: Collaboratively establish clear goals and priorities with the employee. Help them identify critical tasks and deadlines, and assist in organizing their workload effectively. Teach them how to prioritize tasks based on importance, urgency, and dependencies.

  • Time Management Training: Provide training or workshops on time management techniques and tools. This can include strategies for planning, organizing, and scheduling work, as well as methods for dealing with distractions and improving focus.

  • Regular Check-ins and Feedback: Conduct regular check-ins to review progress, discuss challenges, and provide constructive feedback. Offer guidance on improving time management skills, setting realistic expectations, and identifying areas for growth.

  • Delegation and Collaboration: Encourage the employee to delegate tasks when appropriate and collaborate with colleagues to share the workload. Help them develop effective communication and collaboration skills to work efficiently with others.

  • Empowerment and Autonomy: Gradually increase the employee’s autonomy and decision-making authority as they demonstrate improved time management skills. Provide opportunities for them to take ownership of projects and make independent decisions within their role.

By providing the necessary support, mentorship, and guidance, you can help the struggling employee develop better time management habits and set them up for a successful and fulfilling career within the organization.

Tech failure stories

  • Leaky Data due to Preprocessing Mistake due to feature normalized
    • In a machine learning project, the team made a classic mistake during the preprocessing stage of the data. The team was working on a binary classification problem and decided to perform data preprocessing on the entire dataset, including both the training and test sets. Their preprocessing steps involved feature engineering, scaling, and encoding categorical variables.
    • Unbeknownst to the team, they unintentionally introduced data leakage, which occurs when information from the test set is used in the preprocessing stage, leading to over-optimistic model performance during evaluation.
    • The problem arose because they applied feature engineering techniques that required knowledge of the target variable, such as calculating aggregations or statistics based on the entire dataset. As a result, the preprocessing steps unintentionally incorporated information from the test set, compromising the independence between the training and test data.
    • The consequence of this mistake became apparent during model evaluation. The trained model performed exceptionally well on the test set, achieving high accuracy and other evaluation metrics. However, this performance was misleading because the model had “learned” information from the test set that it would not have access to in real-world scenarios.
    • When the team deployed the model in a production environment, it failed to deliver the expected performance. The model was unable to generalize well to new, unseen data, leading to poor predictions and unsatisfactory results. The team realized that the initial preprocessing mistake had compromised the integrity of their training and evaluation process.
    • To address this issue and prevent similar failures in the future, the team took the following steps:
    • Strict Separation of Training and Test Sets: They revised their approach to ensure a strict separation between the training and test sets. Preprocessing steps were performed solely on the training data, and any transformations requiring target-related information were excluded.
    • Pipeline and Cross-Validation: The team implemented a preprocessing pipeline that encapsulated all preprocessing steps, ensuring consistency and reproducibility. They also incorporated cross-validation techniques to evaluate the model’s performance more robustly and detect potential issues early on.
    • Regular Validation and Monitoring: They established a regular validation process to assess model performance on new data and implemented monitoring mechanisms to detect any anomalies or unexpected performance changes.
    • By learning from this failure and implementing these corrective measures, the team was able to improve their data preprocessing practices and mitigate the risk of data leakage. This failure story highlights the importance of maintaining proper data separation and following best practices during preprocessing to ensure the integrity and reliability of machine learning models.

  • Failure due to Large Batch Size

  • In a deep learning project, the team encountered a failure caused by using a batch size that was too large during model training. The team was working on a computer vision task, training a convolutional neural network (CNN) on a large dataset of images.

  • To expedite training and take advantage of parallel processing, the team decided to use a large batch size during training. They believed that using a larger batch size would lead to faster convergence and improved training efficiency. However, this decision had unintended consequences that resulted in a tech failure.

  • As the training process commenced, the team observed several issues and experienced the following problems:

  • Memory Overflow: The large batch size consumed a significant amount of memory, exceeding the capacity of the available hardware resources. This led to memory overflow errors and caused the training process to crash or become unstable.
  • Slower Training: Contrary to their expectations, the training process became slower instead of faster. The large batch size required longer computation time per batch, resulting in increased training time per epoch.
  • Decreased Generalization: The model trained with a large batch size struggled to generalize well to new, unseen data. The overly large batch size hindered the model’s ability to capture diverse patterns and variations present in the dataset, leading to suboptimal performance on the validation and test sets.
  • Gradient Noise: The use of a large batch size introduced noise into the gradient estimates during the optimization process. This noise made it difficult for the model to converge to an optimal solution and resulted in unstable training dynamics.

  • To address these issues and overcome the failure caused by the large batch size, the team took the following corrective measures:

  • Batch Size Optimization: They carefully evaluated the hardware resources available and determined an optimal batch size that would fit within memory constraints while maintaining reasonable training speed and generalization performance. This involved balancing the trade-off between computation efficiency and model performance.
  • Mini-Batch Training: Instead of using a single large batch, the team switched to mini-batch training, where the dataset was divided into smaller batches. This approach allowed for better utilization of hardware resources, reduced memory consumption, and improved convergence.
  • Learning Rate Adjustment: The team adjusted the learning rate of the optimizer to accommodate the smaller batch size. A smaller learning rate helped stabilize the training process and mitigate the noise introduced by the mini-batches.
  • Regularization Techniques: To address the decreased generalization caused by the large batch size, the team employed regularization techniques such as dropout, weight decay, or data augmentation. These techniques helped improve the model’s ability to generalize to new data.

  • By recognizing the issues caused by the large batch size and implementing these corrective measures, the team successfully resolved the tech failure and achieved better training performance and model generalization.

  • This tech failure story serves as a reminder that choosing an appropriate batch size is crucial in deep learning tasks. It is important to consider hardware limitations, training efficiency, and model generalization when determining the batch size to ensure successful and effective training.

  • Tech Failure Story: Optimization Bias Towards Click Bait due to Objective Function

In a digital advertising platform, a tech failure occurred due to an unintended bias in the objective function used for optimizing ad recommendations. The platform aimed to maximize user engagement and click-through rates (CTR) to generate higher revenue. However, the choice of the objective function inadvertently led to an optimization bias towards click bait content.

The platform initially used a simple objective function that directly maximized the number of clicks an ad received. This approach seemed intuitive, as more clicks generally indicated higher user engagement and potentially higher revenue for the platform. However, it failed to account for the quality and relevance of the content being promoted.

As a result, the platform started recommending click bait content, which relied on sensationalized or misleading headlines to attract clicks. While these ads generated high click-through rates initially, users often felt deceived or dissatisfied with the content they encountered after clicking. This led to decreased user trust, increased ad fatigue, and ultimately reduced long-term engagement on the platform.

The consequences of this failure were twofold:

  1. Negative User Experience: Users were lured into clicking on sensationalized ads, only to find that the content did not meet their expectations. This resulted in a poor user experience, diminished trust in the platform, and potentially led to user churn.

  2. Decreased Advertiser Satisfaction: Advertisers promoting legitimate and high-quality content suffered from decreased visibility and engagement because their ads were overshadowed by click bait content. Advertisers became dissatisfied with the platform’s performance and questioned its effectiveness in reaching their target audience.

To rectify this tech failure and address the optimization bias towards click bait, the platform took the following corrective measures:

  1. Revised Objective Function: The platform reevaluated its objective function to incorporate a more comprehensive assessment of user engagement. Instead of solely maximizing clicks, the objective function was adjusted to consider metrics such as time spent on site, conversion rates, and user feedback to prioritize relevant and valuable content.

  2. Quality Control and Monitoring: The platform implemented stricter guidelines and review processes to ensure that ad content adhered to ethical standards and avoided misleading or click bait practices. Regular monitoring and audits were conducted to identify and remove click bait ads from the platform.

  3. User Feedback Integration: User feedback was actively collected and utilized to improve ad relevance and quality. Feedback mechanisms such as user ratings, reviews, or reporting mechanisms were implemented to allow users to report inappropriate or misleading content, which helped in identifying and taking action against click bait ads.

  4. Collaboration with Advertisers: The platform fostered closer collaboration with advertisers, providing them with guidelines for creating engaging, relevant, and ethical ad content. This collaboration aimed to ensure that advertisers understood the platform’s commitment to delivering a positive user experience and discouraged the use of click bait tactics.

By addressing the optimization bias and focusing on delivering valuable and relevant content to users, the platform was able to restore user trust, enhance advertiser satisfaction, and create a healthier advertising ecosystem.

This tech failure story emphasizes the importance of carefully defining objective functions in optimization processes. It highlights the need to consider not only short-term metrics such as clicks but also long-term user satisfaction, ethical considerations, and overall business goals to avoid unintended consequences and optimize for a positive user experience.

####### 1 point 3 acre

of ML1: past ML project deep dive interspersed with some basic ML knowledge: how to deal with imbalanced classes, how to do model auto-retrain, how to debug when online and offline results are inconsistent, etc.

ML2: Design a local business recommendation system. Some questions were asked in the middle such as wide and deep learning. If the model file is very large, which part do you think caused it?

System design: Design a people you may know system.

Round 1: ML application to recommend user groups to advertisers based on user’s interest and advertiser’s categories. At first, I didn’t know that any advertiser information could not be used to generate embeddings. The interviewer later reminded that the data of advertiser engagement was also very important. If you want to recommend more users by category, you actually want to classify users according to their interests and then map them to the category of advertiser products. You can use the data of user interaction with other contents as labels to solve the problem of insufficient supervised data.

During the interview, I was asked about finding similar images given a set of images and a target image. The interviewer mentioned that I could define the metrics for similarity myself.

In this scenario, the task is to identify images that are similar to the target image based on certain criteria. The specific metrics for determining similarity would depend on the requirements and the nature of the images being considered.

Some possible metrics for image similarity could include:

  1. Euclidean Distance: Calculating the Euclidean distance between the target image and each image in the set based on their pixel values. Images with smaller Euclidean distances would be considered more similar.

  2. Feature Extraction: Using pre-trained deep learning models such as convolutional neural networks (CNNs) to extract features from the images. Similarity can then be determined by comparing the extracted feature vectors using techniques like cosine similarity or Euclidean distance.

  3. Structural Similarity Index (SSIM): SSIM compares the structural information between two images, taking into account luminance, contrast, and structural similarity. Higher SSIM values indicate higher similarity.

The choice of metric would depend on factors such as the nature of the images, the available computational resources, and the desired level of accuracy.

During the interview, I would further discuss these metrics, provide examples, and explain how they can be applied to find similar images based on the given target image.

Designing a chat manager from scratch involves implementing features to manage conversations among multiple users. The following phases outline the required functionality:

Phase 1: Implement the following features:

  1. createConversation: Given one or multiple user IDs, create a conversation involving all the users.
  2. deleteConversation: Given a conversation ID, delete the conversation.
  3. printAllConversations: Print all conversations and the users in each conversation.

Note: If createConversation is called multiple times with the same set of users, it should create only one conversation.

Phase 2: After creating the conversations, users can send messages within the conversations. Implement the following features:

  1. sendMessage: Send a message in a conversation.
  2. deleteMessage: Delete a message.
  3. saveMessage: Save a message.
  4. printAllMessages: Print all messages in the conversations.

Phase 3: If the input contains multiple lines, where each line has the following format: [conversationID][userID][message], parse the input data and create all the conversations that appear in the input lines. Also, send all the messages in the corresponding conversations.

During the interview, I would discuss the overall design of the chat manager, including the data structures and algorithms required to implement the features. I would also consider potential edge cases, scalability, and performance considerations.

Please let me know if you have any specific questions or if there’s anything else I can assist you with.

In the 30-minute question, I asked three applied ML questions. I basically asked for all the links of the recommendation system, including model debugging, offline-online inconsistency, light ranking, A/B testing, heavy ranking, two-tower, etc.

For the Pure BQ question, it covered ML foundations, and the question was very detailed. It included how to judge batches.

During the interview, I would carefully consider the requirements of each question, discuss relevant ML concepts and techniques, and provide solutions based on my knowledge and experience.

If you have any specific questions or need further assistance, please let me know.

NBA deep dive

  • Baseline model with Naive Bayes : which assumes no correlation between features
  • If the naive baseline Next Best Action (NBA) music model is not personalized and recommends generic actions, it would have a simpler design. Here’s an overview of how it would look:
  1. Candidate Generation:
    • Predefined Actions: Define a set of generic actions that can be recommended to users, such as “Play Popular Songs,” “Discover New Releases,” “Create Playlist,” or “Browse Genres.”
    • Static Features: Use static features that are not personalized, such as overall popularity of songs, general trends, or fixed playlists.
    • Ranking: Rank the predefined actions based on their predefined weights or popularity.
  2. Candidate Filtering:
    • Filter Actions: Apply basic filters to exclude actions that are not relevant or available to the user based on factors like subscription level, location, or device compatibility.
  3. Top-N Selection:
    • Select Top-N: Select the top-N actions with the highest predefined weights or popularity scores as the recommended actions.
  4. Ranking
    • Naive Bayes is trained on historical data to figure out what to recommend in what order to get conversion.
  5. Serving:
    • Serve the recommended generic actions to the user through the music app’s user interface.
    • Monitor User Feedback: Collect user feedback on the recommended actions to understand user preferences and improve the system over time.

this approach lacks personalization, it can still provide general recommendations to users who have not provided explicit preferences or when personalization is not a requirement. It is a simpler and more straightforward model that can be easily implemented and does not require complex algorithms or large amounts of user data. However, the recommendations may not be as tailored or relevant to each user’s specific preferences.

  • Next iteration is with a DNN:
  1. Candidate Generation:
    • User Data: Collect relevant user data, such as listening history, liked songs, playlists, and demographic information.
    • Feature Extraction: Extract features from the user data that are informative for the recommendation task. These features can include genre preferences, artist affinity, time of day, user location, or any other relevant information.
    • Data Preparation: Prepare the data by encoding categorical variables, normalizing numerical features, and handling missing values.
    • Model Training: Train a DNN model using the prepared data. The DNN model can consist of multiple layers, such as input layers, hidden layers, and output layers, with nonlinear activation functions.
    • Model Evaluation: Evaluate the trained DNN model using appropriate evaluation metrics, such as precision, recall, or area under the ROC curve, to assess its performance.
  2. Candidate Retrieval:
    • User Context: Capture the current user context, such as the user’s device, session, location, or any other relevant contextual information.
    • Feature Extraction: Extract features from the user context that can be used to personalize the recommendations.
    • Data Encoding: Encode the user context features in a format suitable for the model input.
    • Model Inference: Apply the DNN model to the encoded user context features to generate a probability score for each potential action.
    • Sorting: Sort the potential actions based on the predicted probabilities to determine the ranking.
  3. Candidate Ranking:
    • Additional Ranking Features: Incorporate additional ranking features, such as popularity, relevance to the user’s current activity, or freshness of the content.
    • Weighting and Scoring: Assign weights to each feature and compute a final score for each action by combining the DNN model score with the ranking features.
    • Top-N Selection: Select the top-N actions with the highest scores as the recommended next best actions.
  4. Fine Ranking and Filtering:
    • Apply additional filtering or business rules to further refine the list of recommended actions, such as excluding actions that are not suitable for the user’s subscription level or removing actions that the user has recently performed.
    • Apply diversity techniques to ensure a variety of recommended actions, such as diversity in genre, artist, or playlist recommendations.
  5. Serving:
    • Serve the recommended actions to the user through the music app’s user interface.
    • Monitor User Feedback: Collect user feedback on the recommended actions, such as user interactions, clicks, or explicit feedback, to improve the system’s performance over time.
    • Continuous Learning: Periodically retrain the DNN model using new data to adapt to changing user preferences and improve recommendation accuracy.

Using a DNN model allows for more complex representations and nonlinear relationships in the NBA system, potentially leading to improved recommendation accuracy and personalization. However, it also requires larger amounts of training data, longer training times, and more computational resources compared to simpler models like logistic regression.

Item to Item deep dive : FEDERATED LEARNING

  • Content-Based Approach: In a content-based approach, item vectors are created based on the attributes or features of the items. For example, in the case of music, the item vectors can be created using features like genre, artist, album, and musical characteristics (e.g., tempo, rhythm, instrumentation). Similarly, for retail products like lipsticks, item vectors can be created based on attributes like brand, color, ingredients, and product description.
  • Collaborative Filtering Approach: In a collaborative filtering approach, item vectors are created based on the patterns of user interactions with the items. This approach relies on user-item interaction data, such as user ratings, purchase history, or click-through data. The item vectors are generated by considering the preferences and behavior of users who have interacted with those items. Techniques like matrix factorization or deep learning models can be used to learn item embeddings based on the observed user-item interactions.
  • Hybrid Approaches: Hybrid approaches combine both content-based and collaborative filtering techniques to create item vectors. They leverage both the item attributes and the user-item interaction data to capture the characteristics and preferences of items.
  • Baseline: serve recommendations from ANN scores
    • Train the ANN: Build and train the ANN model using historical user-item interaction data or other relevant features. The ANN is trained to learn patterns and make predictions based on the input data.
    • Generate Scores: Once the ANN model is trained, you can use it to generate recommendation scores for a set of candidate items. The input to the ANN can include user-specific information, item features, or any other relevant data that the model has been trained on.
    • Rank Items: Sort the candidate items based on their recommendation scores in descending order. Higher scores indicate higher predicted relevance or preference for the user.
    • Select Top-K Recommendations: Choose the top-K items from the ranked list as the recommendations to be shown to the user. The value of K can be determined based on business requirements or user experience considerations.
    • Serve Recommendations: Present the selected recommendations to the user through the desired interface, such as a website, mobile app, or email.
  • Certainly! Here’s an architecture that combines federated learning and item-to-item recommendations for a music and retail hybrid system:
  1. Data Preparation:
    • Music Data: The music team collects user-item interaction data, such as user listening history, song preferences, and playlists.
    • Retail Data: The retail team collects user-item interaction data related to product views, purchases, and user preferences.
    • Data Preprocessing: Each team preprocesses their respective data to extract relevant features and prepare it for model training.
  2. Federated Learning Setup:
    • Federated Learning Framework: Both teams adopt a federated learning framework, such as TensorFlow Federated or PySyft, to enable collaborative model training while preserving data privacy.
    • Model Definition: Each team defines their own item-to-item recommendation model architecture suitable for their domain, such as deep neural networks or matrix factorization models.
    • Initial Model Synchronization: The teams agree on an initial set of shared model parameters or embeddings to start the federated learning process.
  3. Federated Learning Rounds:
    • Local Model Training: Each team trains their local model using their own data and the shared initial model parameters. They utilize their specific algorithms and loss functions to optimize their models.
    • Weight Update Computation: After local training, each team computes the weight updates by calculating the gradients of the loss function with respect to their model parameters.
    • Secure Weight Update Sharing: The weight updates are securely shared between the teams using privacy-preserving techniques (e.g., homomorphic encryption or secure multi-party computation).
    • Weight Aggregation: The received weight updates from all teams are aggregated using an aggregation algorithm, such as weighted averaging or federated averaging.
    • Weight Update Application: The aggregated weight updates are applied to each team’s local model to update the model parameters.
    • Iterative Process: The federated learning rounds continue iteratively, allowing the models to learn and improve by incorporating the collective knowledge while respecting data privacy.
  4. Item-to-Item Recommendation:
    • Music Recommendation: The music team utilizes their updated model to generate item-to-item recommendations for songs or music tracks based on the learned embeddings.
    • Retail Recommendation: The retail team uses their updated model to provide item-to-item recommendations for retail products based on the learned embeddings.
    • Recommendation Service Integration: The recommendations from both teams can be integrated into a single recommendation service that combines music and retail recommendations.
  5. User Experience:
    • Users receive personalized recommendations based on their music preferences and retail behavior, enhancing their overall experience.
    • User Feedback Collection: User feedback and interactions with the recommended items are collected to further improve the models and provide continuous learning.

By combining federated learning with item-to-item recommendations, this architecture allows collaborative learning between music and retail teams while respecting data privacy. Each team trains their local models using their respective data, and the federated learning process enables the sharing and aggregation of model updates. The learned embeddings from the models can then be used to generate personalized item-to-item recommendations for both music and retail domains.