There is an extended class of web applications that involve predicting user responses to options. Such an installation is called a recommender system. In this article, I’ll walk you through what a recommendation system in machine learning is and how it works. You will also get hands-on projects based on Recommendation Systems at the end of this article.
I’ll start this article with an overview of the most important examples of a recommender system. However, to highlight the problem, two good examples of recommender systems are:
- Offering news articles to the online newspaper readers, based on the predictions of the interest of a reader.
- Provide online retailer customers with suggestions on what they might want to buy, based on their purchasing history and/or product searches.
Types of Recommendation System
A recommendation system uses several different technologies. We can classify these systems into two large groups:
- Content-Based Systems: Examine the properties of recommended items. For example, if you have watched a lot of cowboy movies on Netflix, then Netflix will always recommend a movie to you that is classified as a cowboy genre.
- Collaborative filtering systems: Here the recommend items are based on measures of similarity between users and/or items. Items that are recommended to a user are those items that are preferred by similar users.
However, these two types of recommendation systems in themselves are not that sufficient as some new algorithms have proven more effective for building a recommendation system.
How does a Recommendation System work?
In an application based on a recommendation system, there are two classes of entities, which we will call users and items. Users have preferences for certain items, and those preferences must be extracted from the data.
The data itself is represented in the form of a utility matrix, giving for each user-item pair a value that represents what is known about that user’s degree of preference for that item. The values come from an ordered set, for example, integers 1 through 5 representing the number of stars the user gave us a rating for that item.
We assume that the matrix is sparse, which means that most of the entries are “unknown”. An unknown rating means that the recommendation engine will not have any data about the user’s preferences for the item.
The recommendation in the physical world is quite simple. First, it is not possible to tailor the store to each individual customer. Thus, the selection of what should be recommended is governed only by the aggregated numbers.
Typically, a bookstore will only display the most popular books, and a newspaper will only print the articles that it thinks will be of most interest. In the first case, sales figures govern choices, in the second, editorial judgment is used.
Machine Learning Projects Based on Recommendation Systems
Now let’s have a look at some popular and very useful examples of a recommendation system. The Projects mentioned below are solved and explained properly and are well optimized to boost your machine learning portfolio.
Perhaps the most important use of recommendation systems is that of online retailers. We have noted how Amazon or similar online vendors go to great lengths to present every recurring user with suggestions for products they might want to purchase.
These suggestions are not random but are based on buying decisions made by similar customers or other techniques. I created a fashion recommendation system based on product recommendations. You can find the project here.
Netflix offers its customers movie recommendations they might like. These recommendations are based on reviews provided by users. The importance of accurately predicting ratings is so high that Netflix offered a million-dollar prize for the first algorithm that could beat its own recommendation system by 10%.
The prize was finally won in 2009, by a team of researchers called “Bellkor’s Pragmatic Chaos”, after more than three years of competition. I made a project based on Movie Recommendations which you can find here.
I hope you liked this article based on Recommendation Systems with Machine Learning. Feel free to ask your valuable questions in the comments section below.