Introduction to Recommender Systems: Non-Personalized and Content-Based

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

Introduction

### Course Review: Introduction to Recommender Systems: Non-Personalized and Content-Based In today's digital age, we are inundated with choices, be it movies, books, or products. As a result, the technology behind recommender systems has gained immense importance in helping users navigate through this abundance. Coursera's course titled **"Introduction to Recommender Systems: Non-Personalized and Content-Based"** is a comprehensive and insightful starting point for anyone looking to understand the foundation of this crucial technology. #### Overview This course serves as the first module in the Recommender Systems specialization, breaking down the concept of recommender systems into digestible sections. It covers non-personalized recommendation techniques, demographic-based approaches, and content-based filtering. By the end of the course, participants will have practical skills and foundational knowledge in computing recommendations from datasets. #### Course Structure and Content The syllabus of the course is meticulously structured, guiding learners through the essential aspects of recommender systems: 1. **Preface**: The course kicks off with an introduction that places the technology in a historical context, offering an overview of what participants can expect. This foundational knowledge sets the stage for the more technical content that follows. 2. **Introducing Recommender Systems**: Here, learners delve deeper into the taxonomy of recommender systems, exploring notable examples like MovieLens and Amazon.com. This module concludes with an assessment that reinforces understanding of the core concepts. 3. **Non-Personalized and Stereotype-Based Recommenders**: This section focuses on non-personalized recommendations, teaching techniques such as summary statistics and product associations. An assignment using spreadsheets and a quiz ensure that learners can apply what they've learned. 4. **Content-Based Filtering – Part I & II**: Over two weeks, this part covers the development of personal interest profiles and advanced computational techniques for content-based filtering. The assignments here are engaging, requiring participants to utilize spreadsheets for real-world scenarios, and examine three types of profiles for predictive analysis. 5. **Course Wrap-up**: As the course concludes, there’s a focus on mathematical notation that will aid learners as they advance in the specialization. #### Learning Experience The course is designed to be engaging, combining theoretical knowledge with practical assignments. The use of spreadsheets for hands-on assignments helps bridge the gap between abstract concepts and real-world applications, allowing students to get their hands dirty in a manageable way. The quizzes serve as good checkpoints to gauge comprehension, ensuring students stay on track and grasp essential information before moving on. The inclusion of familiar platforms like MovieLens and Amazon.com makes the content relatable and contextual. The emphasis on both non-personalized and content-based filtering provides a well-rounded introduction, fostering a broad understanding of several recommendation methodologies. #### Recommendation I highly recommend **"Introduction to Recommender Systems: Non-Personalized and Content-Based"** for anyone interested in data science, machine learning, or user experience design. Whether you're a beginner looking to explore the field or a professional seeking to enhance your skill set, this course lays down a solid foundation. Its structured approach makes complex concepts accessible and provides practical skills that can be applied in real-world scenarios. Taken alongside the specialization's subsequent courses, this course equips learners well for a deeper dive into more advanced recommender system techniques. In summary, this course not only educates but also empowers learners to create their own recommendations, a pertinent skill in our data-driven world. Don’t miss the opportunity to immerse yourself in the realm of recommender systems through this thoughtful and well-crafted course!

Syllabus

Preface

This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization.

This module introduces recommender systems in more depth. It includes a detailed taxonomy of the types of recommender systems, and also includes tours of two systems heavily dependent on recommender technology: MovieLens and Amazon.com. There is an introductory assessment in the final lesson to ensure that you understand the core concepts behind recommendations before we start learning how to compute them.

In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension.

The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems.

The assessments for content-based filtering include an assignment where you compute three types of profile and prediction using a spreadsheet and a quiz on the topics covered. The assignment is in three parts -- a written assignment, a video intro, and a "quiz" where you provide answers from your work to be automatically graded.

We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization).