E-commerce Recommendation Systems with Machine Learning Training Course

Module 1: Foundations of Recommendation Systems

• Evolution of recommendation: From simple popularity lists to personalized discovery
• Understanding user feedback: Differentiating between Explicit (ratings) and Implicit (clicks, views) data
• The anatomy of a recommendation pipeline: Candidate generation, scoring, and re-ranking
• Business impact of recommendation: Long-tail discovery and customer lifetime value (CLV)
• Ethical considerations: Avoiding echo chambers and ensuring algorithmic fairness
• Practical session: Performing Exploratory Data Analysis (EDA) on an e-commerce transaction dataset

Module 2: Content-Based Filtering and Metadata Extraction

• Leveraging product attributes: Categories, descriptions, and price points
• Text vectorization for items using TF-IDF and Bag-of-Words
• Calculating item similarity using Cosine Similarity and Euclidean Distance
• Building user profiles based on historical item interactions
• Advantages and limitations of content-based systems in diverse catalogs
• Practical session: Building a "Similar Products" engine using product metadata and descriptions

Module 3: Memory-Based Collaborative Filtering

• User-User Collaborative Filtering: Identifying and leveraging similar shoppers
• Item-Item Collaborative Filtering: Predicting preferences based on item relationships
• Similarity measures: Pearson Correlation vs. Jaccard Similarity
• The K-Nearest Neighbors (KNN) approach for recommendation
• Handling neighborhood selection and weighting in large datasets
• Practical session: Implementing an Item-Item collaborative filter from scratch using NumPy and Pandas

Module 4: Model-Based Collaborative Filtering and Matrix Factorization

• Introduction to Latent Factor Models: Discovering hidden patterns in user behavior
• Singular Value Decomposition (SVD) and its application in e-commerce
• Alternating Least Squares (ALS) for handling large-scale implicit datasets
• Regularization techniques to prevent overfitting in sparse matrices
• Comparing memory-based vs. model-based approaches for speed and accuracy
• Practical session: Using the Surprise library to build and tune an SVD-based recommender

Module 5: Hybrid Recommendation Strategies

• Why hybrid systems? Overcoming the limitations of individual algorithms
• Weighted hybrids: Combining scores from content and collaborative models
• Switching and cascading hybrids: Conditional logic for recommendation
• Feature augmentation: Using one model's output as input for another
• Implementing Meta-level hybrids for complex e-commerce environments
• Practical session: Developing a hybrid model using the LightFM library to combine user-item interactions with metadata

Module 6: Solving the Cold Start and Sparsity Problems

• Defining the Cold Start: New users, new items, and new platform challenges
• Strategies for new users: Onboarding surveys and popularity-based fallbacks
• Strategies for new items: Utilizing content-based features until interaction data is available
• Dealing with Data Sparsity: Techniques for densifying the user-item matrix
• Exploration vs. Exploitation: Introducing randomness to discover new user interests
• Practical session: Designing a "New Arrival" logic that integrates new products into the recommendation feed

Module 7: Deep Learning for Recommendation Systems

• Introduction to Neural Collaborative Filtering (NCF)
• Using Wide & Deep architectures to balance memorization and generalization
• Sequence models: Using RNNs and LSTMs to predict the "Next Best Action"
• Transformer-based recommenders: Leveraging self-attention for session-based data
• Understanding the role of embeddings in capturing high-dimensional relationships
• Practical session: Building a session-based recommender using TensorFlow Recommenders (TFRS)

Module 8: Re-ranking and Learning to Rank (LTR)

• The transition from scoring to ranking: Why top-k accuracy matters
• Introduction to Pointwise, Pairwise, and Listwise ranking approaches
• Integrating business constraints: Stock levels, profit margins, and diversity in the final list
• Feature engineering for ranking models: User context, time of day, and device type
• Using Gradient Boosted Trees (XGBoost/LightGBM) for final candidate re-ranking
• Practical session: Implementing a LambdaMART model to rank product search results for an e-commerce store

Module 9: Evaluation Metrics and A/B Testing

• Offline metrics: RMSE, MAE, Precision@K, Recall@K, and nDCG
• Online evaluation: Designing and executing robust A/B tests for recommendation
• Understanding "Novelty," "Diversity," and "Serendipity" in user experience
• Business KPIs: Measuring lift in Average Order Value (AOV) and Conversion Rate
• Interpreting results and avoiding common pitfalls in online experimentation
• Practical session: Creating an evaluation dashboard to compare three different recommendation strategies

Module 10: Productionizing and Scaling Recommender Systems

• Architecture for real-time recommendation: Databases, caches, and API layers
• Handling high-velocity data: Incremental model updates vs. batch retraining
• Cloud deployment: Utilizing AWS Personalize or GCP Recommendations AI
• Monitoring model performance and detecting "Data Drift" in production
• Scaling to millions of users: Using Approximate Nearest Neighbors (ANN) like Faiss
• Practical session: Simulating a production deployment and testing API latency for real-time suggestions

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Phoenix Training Center admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@phoenixtrainingcenter.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@phoenixtrainingcenter.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254725012095 / +254724452588