How do we buy things in our day-to-day lives? We ask our friends, research the product specifications, compare the product with similar products on the Internet, read the feedback from anonymous users, and then we make decisions. How would it be if there is some mechanism that does all these tasks automatically and recommends the products best suited for you efficiently? A recommender system or recommendation engine is the answer to this question.
In this introductory chapter, we will define a recommender system in terms of the following aspects:
Helping to develop an understanding of its definition
Explaining its basic functions and providing a general introduction of popular recommender systems
Highlighting the importance of evaluation techniques
Have you ever given a thought to the "People you may know" feature in LinkedIn or Facebook? This feature recommends a list of people whom you might know, who are similar to you based on your friends, friends of friends in your close circle, geographical location, skillsets, groups, liked pages, and so on. These recommendations are specific to you and differ from user to user.
Recommender systems are the software tools and techniques that provide suggestions, such as useful products on e-commerce websites, videos on YouTube, friends' recommendations on Facebook, book recommendations on Amazon, news recommendations on online news websites, and the list goes on.
The main goal of recommender systems is to provide suggestions to online users to make better decisions from many alternatives available over the Web. A better recommender system is directed more towards personalized recommendations by taking into consideration the available digital footprint of the user and information about a product, such as specifications, feedback from the users, comparison with other products, and so on, before making recommendations.
In this book, we will learn about popular recommender systems that are used the most. We will also look into different machine learning techniques used when building recommendation engines with sample code.
The book is divided into 5 chapters:
In Chapter 1, Getting Started with Recommender Systems, you will get a general introduction to recommender systems, such as collaborative filtering recommender systems, content-based recommender systems, knowledge-based recommender systems, and hybrid systems; it will also include a brief definition, real-world examples, and brief details of what one will be learning while building a recommender system.
In Chapter 2, Data Mining Techniques Used in Recommender Systems, gives you an overview of different machine learning concepts that are commonly used in building a recommender system and how a data analysis problem can be solved. This chapter includes data preprocessing techniques, such as similarity measures, dimensionality reduction, data mining techniques, and its evaluation techniques. Here similarity measures such as Euclidean distance, Cosine distance, Pearson correlation are explained. We will also cover data mining algorithms such as k-means clustering, support vector machines, decision trees, bagging, boosting, and random forests, along with a popular dimensional reduction technique, PCA. Evaluation techniques such as cross validation, regularization, confusion matrix, and model comparison are explained in brief.
In Chapter 3, Recommender Systems, we will discuss collaborative filtering recommender systems, an example for user- and item-based recommender systems, using the
recommenderlabR package, and theMovieLensdataset. We will cover model building, which includes exploring data, splitting it into train and test datasets, and dealing with binary ratings. You will have an overview of content-based recommender systems, knowledge-based recommender systems, and hybrid systems.In Chapter 4, Evaluating the Recommender Systems, we will learn about the evaluation techniques for recommender systems, such as setting up the evaluation, evaluating recommender systems, and optimizing the parameters.
In Chapter 5, Case Study – Building Your Own Recommendation Engine, we will understand a use case in R, which includes steps such as preparing the data, defining the rating matrix, building a recommender, and evaluating and optimizing a recommender.
