Basic Recommender Systems

EIT Digital via Coursera

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

Introduction

### Course Review: Basic Recommender Systems on Coursera In today's digital world, personalization is key to enhancing user experiences. From streaming services to online retail, recommender systems play a vital role in shaping our interactions with technology. The **Basic Recommender Systems** course offered on Coursera is a comprehensive introduction to the fundamental principles and practices that underpin these powerful tools. #### Course Overview The **Basic Recommender Systems** course provides a solid foundation by exploring both collaborative and content-based approaches. It covers essential algorithms that serve as the backbone of recommendation engines, teaching you not only how they function but also how to implement and evaluate these systems effectively. Throughout the course, you'll gain insights into the strengths and limitations of various recommender system alternatives, empowering you to make informed decisions in real-world applications. #### Syllabus Breakdown 1. **Basic Concepts** The initial module lays the groundwork, familiarizing you with core concepts in recommender systems. You'll learn to classify and analyze different families of algorithms based on specific input data. By the end of this module, you will be equipped to choose the most suitable algorithm for your particular needs, which is crucial for optimizing the recommendation process. 2. **Evaluation of Recommender Systems** Understanding the effectiveness of your recommender system is essential. This module dives into the metrics and evaluation methods used to measure system quality. You'll learn how to define the evaluation activities necessary based on your goals, enabling you to assess the performance of your recommender system accurately. 3. **Content-Based Filtering** This module focuses on content-based recommender techniques. By using an Item-Content Matrix (ICM), you'll explore how to recommend items similar to those a user has previously liked. The course will guide you through different similarity functions and the importance of normalizing and tuning item attributes within the ICM. By the end, you'll be equipped to build a content-based recommender system capable of producing high-quality recommendations. 4. **Collaborative Filtering** Collaborative filtering techniques are the heart of many recommendation systems used today. This module covers how to construct user rating matrices (URM) to analyze user-item interactions and provides strategies for building non-personalized systems. You'll learn to normalize the URM for improved accuracy and choose the most appropriate similarity functions—skills essential for tackling challenges associated with explicit ratings. #### Review and Recommendation One of the standout features of the **Basic Recommender Systems** course is its structured approach, combining theoretical foundations with practical applications. Each module is designed to build upon the previous one, ensuring a gradual learning curve that caters to both beginners and those with some prior knowledge. The course content is clear and engaging, making complex concepts accessible. The learning outcomes are well-defined, and by the end of the course, you should feel confident in your ability to design and evaluate recommender systems that align with user needs. This course is particularly beneficial for data scientists, software developers, and anyone interested in machine learning applications in personalization. If you're looking to deepen your understanding of recommendation systems and wish to implement effective solutions in your projects or career, I highly recommend enrolling in the **Basic Recommender Systems** course on Coursera. Not only will it expand your skill set, but it will also enhance your ability to create personalized experiences that resonate with users. Whether you're aiming to improve existing systems or create new ones from scratch, this course is an invaluable resource for anyone aspiring to excel in the field of data science and machine learning.

Syllabus

BASIC CONCEPTS

In this first module, we'll review the basic concepts for recommender systems in order to classify and analyse different families of algorithms, related to specific set of input data. At the end, you’ll be able to choose the most suitable type of algorithm based on the data available, your needs and goals. Conversely, you'll know how to select the input data based on the algorithm you want to use.

EVALUATION OF RECOMMENDER SYSTEMS

In this second module, we'll learn how to define and measure the quality of a recommender system. We'll review different metrics that can be used to measure for this purpose. At the end of the module you'll be able to identify the correct evaluation activities required to measure the quality of a given recommender system, based on goals and needs.

CONTENT-BASED FILTERING

In this module we’ll analyse content-based recommender techniques. These algorithms recommend items similar to the ones a user liked in the past. We’ll review different similarity functions and you’ll then be able to choose the more suitable one for your system. The main input is the Item-Content Matrix (ICM) which describes all the attributes for each item. We’ll see how we can improve the quality of content-based techniques, by normalising and tuning the importance of each attribute in the ICM: you’ll be able to use some specific tuning strategies in order to obtain the best quality recommendations from your system. So, at the end of this module, you’ll know how to build a content-based recommender system, how to clean and normalize your input data.

COLLABORATIVE FILTERING

In this module we’ll study collaborative filtering techniques, which use the User Rating Matrix (URM) as the main input data, describing the interaction between users and items. We’ll learn how to build non-personalised recommender systems and how to normalise the URM, in order to provide better recommendations. At the end of the module you’ll be able to select the most appropriate similarity function and the most suitable way to compute similarity, overcoming issues related to explicit ratings.