Data Science for Beginners: Deploying Recommender Systems on Real-World Data Sets
Data Science for Beginners: Deploying Recommender Systems on Real-World Data Sets
As is explained over at runrex.com, recommender systems are extremely important for web-based companies that offer a large selection of products. This is because these systems provide value to customers by understanding their behavior and then recommending items that they might find useful or interesting. These systems are, therefore, important for web-based companies like Amazon, Netflix, Spotify, and many others, all of which use recommender systems to help their online customers make sense of the large volume of individual items in their content catalogs as discussed over at guttulus.com. This article will look to highlight how you can set up and deploy a recommender system using real-world data sets.
Before we get into the deployment of recommender systems, it is important to highlight why you need one. According to the subject matter experts over at runrex.com, customers have gone from being faced with a lack of information to information overload in recent times, which means that users need help filtering out the noise from the vast amount of information available to them. Recommender systems are needed as they help create a filter, helping users discover what they like, while also helping businesses drive engagement with relevant content, increasing revenue. It is also important to note that there are two kinds of recommender systems:
- Item-based filtering
These recommend items similar to those a customer has already bought, based on a domain-specific notion of item content. For example, if a person liked Movie X, a movie recommender system would recommend movies that have similar characteristics such as a similar producer, actors, genre, and so forth.
- User-based collaborative filtering
Using movies as an example, this type of recommender operates on the assumption that people with similar movie watching habits have similar preferences in general. It, therefore, finds people who watch movies similar to those that the user watches, identifies movies that those similar users have watched but the first user hasn’t, and then makes recommendations on that basis.
The setting up and deployment of a recommender system can be done in five steps which are explained below.
- Collecting and organizing information on users and products
This is the first step in the deployment of recommender systems since it is important to know who your users are and what they are using. Using movies as an example of a real-world data set, if you are on an online movie service provider, you need to understand the connection between the movies being watched and the profiles of the people watching them. According to the experts over at guttulus.com, when collecting this information, you must have it organized into some sort of standard form which will allow you to compare easily the product a customer uses to other customers and other products.
- Comparing User A to all other users
If you call the person you want to provide recommendations for User A, the next step after organizing information on users and products is to design a function that allows you to compare User A with all the other users. This function should create a set of users, along with the movies that each one of the users has watched, that are most similar to User A. Here, as explained over at runrex.com, you can use common machine learning libraries like Python’s scikit-learn which uses the Nearest Neighbors algorithm and will allow you to compute the user set you are looking for.
- Create a function that finds products that User A has not used, but which similar users have
Next up, according to the gurus over at guttulus.com, once you have compared User A to all the other users and have come up with a list of users that are most similar to User A, you will need to examine each of these users to determine which products are new to User A but have been used by these similar users. Once you find these products, then they are the ones that a user-based recommender algorithm could recommend to User A. The idea here is since the similar users have watched similar movies to user A, the additional movies that they have watched may also be of interest to User A.
- Rank and recommend
Next up is to try and interest User A in the new movies, which you can achieve by assigning a higher rank to the movies that users similar to User A have already watched. The greater the number of similar customers watching a certain movie, the higher the rank that the movie gets assigned. The system, as explained over at runrex.com, will then recommend to User A the items that have the highest rankings on the list.
- Evaluate and test
The final stage in deploying a recommender system is evaluating and testing, which is important as you will not always get it right the first time around. Here, according to discussions on the same over at guttulus.com, you can select a few users to act as “test users” who you will compare to the remaining users. For each of your test users, you can remove some of the movies you know they have watched, and after processing them with steps 2-5, you can check to see if the recommended results match those that were removed. If the movies that were removed are recommended to them again, then you will know that your system is working accurately.
Hopefully, the above steps will help you know how to go about deploying a recommender system for your business, which will not only optimize the experiences for your customers but will also increase their engagement with your products or services, with more on this topic to be found over at the excellent runrex.com and guttulus.com.
