Personalized recommendations power so many of the digital experiences we encounter every day, from streaming platforms suggesting our next favorite show to online stores curating product suggestions just for us. For data scientists, building these systems is both an art and a science—one that can unlock immense value for businesses and provide deeper, more meaningful user engagement.
In this blog series, we'll delve into the world of recommendation engines, exploring different types of recommenders, evaluation metrics, and practical implementation. We'll cover everything from foundational concepts to hands-on techniques, aiming to make it accessible for data professionals of all experience levels.
Here’s what you can expect from each part of the series:
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Part 1: Types of Recommender Systems – We’ll explore the foundational approaches and types, including collaborative filtering, content-based filtering, and hybrid methods, so you can understand the options and best fit for your use case.
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Part 2: Evaluation Metrics for Recommender Systems – Knowing how well your recommender system performs is crucial. We’ll discuss key metrics, such as precision, recall, and diversity, to evaluate your model’s effectiveness.
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Part 3: Building Your Own Recommendation System: Content-Based – In this hands-on post, we’ll guide you through building a content-based recommender system, leveraging data on users' past behavior and item attributes.
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Part 4: Building Your Own Recommendation System: Collaborative Filtering – We’ll have a practical look at collaborative filtering techniques, from user-based to item-based methods, and how to implement them in real-world applications.
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Part 5: Building Your Own Recommendation System: We will explore some of the advanced techniques likes Deep Learning - Neural Network for Collaborative Filtering as well as building personalized recommender systems with Generative AI (Mistral LLM).
- NLTK for tokenization
- Scikit-learn for cosine similarity
- Scikit-Surprise for experimenting with different recommender systems algorithms
- Gensim for Word Embedding and similarity calculation
- TensorFlow, Keras for building Deep neural network learning based collaborative-filtering model
- Ollama for LLM deployment locally
- FAISS for indexing vector and running similarity search efficiently
