Advanced Recommender Systems

EIT Digital via Coursera

Go to Course: https://www.coursera.org/learn/advanced-recommender-systems

Introduction

**Course Review: Advanced Recommender Systems on Coursera** If you are keen to delve into the world of recommender systems, look no further than the "Advanced Recommender Systems" course offered on Coursera. This comprehensive program is designed for individuals who already possess a foundational understanding of machine learning and are eager to deepen their knowledge in creating more sophisticated recommendation algorithms. The course equips participants with the tools and methodologies necessary to effectively utilize advanced machine-learning techniques for developing scalable and reliable recommender systems. **Course Overview** The course kicks off with a detailed overview of the importance of recommender systems in today’s digital landscape, where users are inundated with choices, and providing personalized suggestions has become crucial for enhancing user experiences. Throughout the course, learners gain insights into automatic model building, leveraging historical user data to enhance predictive accuracy without the need for exhaustive manual intervention. **Syllabus Breakdown** 1. **Advanced Collaborative Filtering:** The first module introduces learners to collaborative filtering methods rooted in machine learning. Participants will learn to craft item-based collaborative algorithms that not only improve recommendations but also align better with actual user perceptions. By minimizing discrepancies between predicted and real user opinions, learners will be equipped to develop robust recommendation strategies. 2. **Singular Value Decomposition Techniques (SVD):** The second module delves into matrix factorization and dimensionality reduction techniques inspired by SVD. By exploring both memory-based and model-based recommender systems, learners will understand the strengths and weaknesses of each approach. Key concepts include the selection of latent features, which is essential for personalizing recommendations and mitigating overfitting—a critical skill for data-driven decision-making. 3. **Hybrid and Context-Aware Recommender Systems:** Module three covers the creation of hybrid systems by amalgamating collaborative filtering with content-based methods. By employing various hybridization strategies, participants learn to enhance user recommendations through enriched context and content information. This module illustrates how basic algorithms can be transformed into powerful hybrid models, thereby significantly improving the quality of recommendations. 4. **Factorization Machines:** The final module introduces Factorization Machines (FMs), a state-of-the-art technique for collaborative filtering that incorporates side information. This segment emphasizes the versatility of FMs, allowing learners to construct models ranging from simple matrix factorization to sophisticated collaborative filtering algorithms. Importantly, the course offers practical insights on balancing different types of input information through weights and coefficients, further enhancing prediction accuracy. **Capstone Project - RecSys Challenge:** The course culminates in the optional RecSys Challenge, which serves as a hands-on opportunity to apply learned concepts in a competitive setting. Participants analyze real-world data from an online supermarket, taking on the task of predicting user interactions with items. While participation in the challenge is not mandatory for course completion, those who successfully engage are awarded an Honors designation on their certificate, adding distinction to their learning experience. **Recommendation:** In conclusion, the "Advanced Recommender Systems" course on Coursera is a remarkable resource for anyone looking to elevate their expertise in the domain of recommendation systems. With its structured syllabus, in-depth modules, and practical applications, the course empowers learners with both theoretical knowledge and practical skills. Whether you are a data scientist, machine learning enthusiast, or a professional looking to integrate recommender systems into your work, this course provides the ideal platform to hone your skills. I highly recommend enrolling in the course to gain valuable insights, enhance your resume, and push the boundaries of what's possible in the realm of personalized recommendations.

Syllabus

ADVANCED COLLABORATIVE FILTERING

In this first module, we will see how to apply machine learning to collaborative filtering techniques. We will learn how to write an item-based collaborative algorithm which is able to automatically learn the best similarities between items, in order to provide improved recommendations that better match the user opinions predicted by the model with the true user opinions. We will also understand how to train collaborative filtering algorithms that minimize this gap. We will finally define a new error metric based on ranking comparisons, useful to design learning-to-rank algorithms.

SINGULAR VALUE DECOMPOSITION TECHNIQUES - SVD

In this second module, we will study a new family of collaborative filtering techniques based on dimensionality reduction and matrix factorization approaches, all inspired by SVD (Singular Value Decomposition). We will see the difference between memory-based and model-based recommender systems, discussing their limitations and advantages. In particular, we will learn how to turn basic matrix factorization algorithms from memory-based into model-based approaches. We will also analyse a new important parameter, the number of latent features. We will learn how to choose the correct number of latent features in order to provide personalised recommendations and to reduce the risk of overfitting historical data.

HYBRID AND CONTEXT AWARE RECOMMENDER SYSTEMS

In this third module, we will see how to combine two or more basic algorithms, such as collaborative filtering and content-based techniques, into a hybrid recommender system, in order improve the quality recommendations. We will study different hybridization approaches, from the simplest heuristic-based, to the more sophisticated machine learning-based. Thanks to hybrid techniques, we will be able to enrich the input of a collaborative recommender system with either content or contextual information.

FACTORIZATION MACHINES

In this fourth and last module, we will introduce a new advanced technique of collaborative filtering with side information, which is called Factorization Machine (FM), and we’ll see how the input data should be represented when using this technique. With only one mathematical model, based on how you build the input table, we will be able to create a simple matrix factorization algorithm or a sophisticated collaborative filtering algorithm with side information (context, attributes on items or attributes on users). We will also discuss benefits and critical issues of algorithms based on FMs. At the end of the module you will know how to use FMs to mix together different kinds of filtering techniques and how to balance different kinds of input information, playing with coefficients and weights, in order to make better and more sophisticated predictions.

Recsys Challenge (Honors)

The RecSys Challenge is the best way to train your competences: it's a practical exercise which provides a "hands-on" opportunity to put to good use and improve what you've been learning during this course (learning by doing). The application domain is an online store, the dataset we provide contains 4 months of transactions collected from an online supermarket. The main goal of the competition is to discover which item a user will interact with. The RecSys Challenge is optional and it is not required to pass the course. If you complete it, you will receive an Honors designation on your Course certificate.