Advanced Recommender System Series
Learn how advanced recommender systems transform data into impactful, tailored recommendations for users.
Recommender systems are more than just algorithms—they’re the backbone of personalized user experiences and a driving force behind business success. From increasing engagement to boosting revenue, the ability to deliver tailored recommendations is a game-changer. With this series, you’ll gain the insights and tools needed to build recommendation systems that excel in both performance and impact.
Last year, we rolled out the "Building Your Own Recommendation System" series, a beginner-friendly, four-part guide that focused on the foundational aspects of recommender systems. In that series, we covered:
The Basics: We started with an overview of what recommender systems are and why they play such a crucial role in today’s digital landscape. From Netflix’s movie suggestions to Amazon’s personalized shopping experience, we explored the impact of recommendation engines in driving user engagement and business growth.
Evaluation Techniques: Knowing how to measure a recommendation system’s performance is as important as building one. We delved into key metrics like precision, recall, mean average precision (MAP), and the area under the curve (AUC), helping readers understand how to assess and improve the quality of their models.
Collaborative Filtering: This technique relies on user-item interactions, making it a cornerstone of personalization. We walked through the implementation of both user-based and item-based collaborative filtering, providing insights into how these methods leverage historical data to predict user preferences.
Content-Based Filtering: Unlike collaborative filtering, this method focuses on metadata—like product descriptions, user profiles, or item attributes—to generate recommendations. We showed how to create feature-rich models capable of making recommendations even in scenarios with limited interaction data.
For those new to recommendation systems, the series served as a comprehensive introduction, combining hands-on implementations with theoretical insights. If you missed it, you can catch up here before diving into this advanced series.
Now, we’re excited to take things to the next level with the Advanced Recommender Blog Series.
About the Writers:
Manisha Arora: Manisha is a Data Science Lead at Google Ads, where she leads the Measurement & Incrementality vertical across Search, YouTube, and Shopping. She has 12 years experience in enabling data-driven decision making for product growth.
Arun Subramanian: Arun is an Associate Principal of Analytics & Insights at Amazon Ads, where he leads development and deployment of innovative insights to optimize advertising performance at scale. He has over 12 years of experience and is skilled in crafting strategic analytics roadmap, nurturing talent, collaborating with cross-functional teams, and communicating complex insights to diverse stakeholders.
What’s in the Advanced Recommender Blog Series?
This new three-part series dives into advanced techniques for building state-of-the-art recommendation systems:
Part 1: Beyond Collaborative and Content-Based Filtering
We’ll start by addressing the limitations of traditional approaches like collaborative and content-based filtering. You’ll also learn about powerful concepts such as embeddings and vectorization, which form the foundation of modern recommendation systems.
Part 2: Neural Network Architectures and Deep Learning
Here, we’ll dive deep into neural networks, explaining how they revolutionize recommendation systems. From architecture design to hands-on code examples, this part will equip you with the skills to build deep learning-based recommenders.
Part 3: LLM-Powered Recommendations with Mistral
Large language models (LLMs) are reshaping recommendation systems by offering unprecedented capabilities for understanding context and generating predictions. In this part, we’ll explore how LLMs, like Mistral, enhance context understanding and prediction capabilities, complete with hands-on code examples.
Who Is This Series For?
This series is designed for:
Data professionals looking to deepen their knowledge and build advanced recommendation systems.
Enthusiasts and practitioners who want to move beyond foundational techniques and explore cutting-edge solutions.
This series is designed for data professionals eager to advance their expertise in recommendation systems and explore state-of-the-art techniques. Whether you’ve mastered the basics or are looking to integrate cutting-edge tools like deep learning and large language models, these posts will provide both in-depth theory and hands-on guidance to help you build next-generation systems that deliver exceptional user experiences.