Recommendation engines

A practical example: recommendation engines

A "numeric top 10" of the most sold products might be a list of the top 10 products ranked by the number of units sold, with the product that sold the most at the top of the list and the product that sold the 10th most at the bottom of the list. Similarly, a "numeric top 5" of the most watched movies might be a list of the top 5 movies ranked by the number of views, with the movie that was viewed the most at the top of the list and the movie that was viewed the 5th most at the bottom of the list.

Numeric tops can be useful for various applications, such as identifying popular products, highlighting trends or patterns, or informing recommendations based on the most popular items. But they also have a lot of disadvantages:

Everyone gets the same list
Does not account individual user preferences
Good selling products sell even more
The niche gets little opportunity

A recommendation engine offers a good solution to eliminate these disadvantages

A recommendation engine, also known as a recommender system, is an AI algorithm that suggests items to users based on their interests and preferences. These items could be anything from products on an e-commerce website to movies on a streaming service.

The recommendation engine works by analyzing data on the user's behavior and interactions with the system. This data might include the items they have viewed or purchased in the past, as well as any ratings or reviews they have provided.

Using this data, the recommendation engine builds a model of the user's preferences and interests. It then uses this model to suggest new items that the user is likely to be interested in. The suggestions may be based on factors such as similarity to previously viewed items, popularity among similar users, or other relevant attributes.

For example, let's say you are browsing an online bookstore and have purchased several science fiction novels in the past. A recommendation engine might use this information to suggest new science fiction novels that you haven't read yet, based on your past behavior and the behavior of similar users.

Recommendation engines can be extremely useful for both users and businesses. Users benefit from personalized recommendations that help them discover new and interesting items, while businesses benefit from increased engagement and sales.

There are several different types of recommendation engines, including collaborative filtering, content-based filtering, and hybridapproaches that combine both methods. Each type has its own strengths and weaknesses, and the choice of which to use will depend on the specific use case and available data.

Collaborative filtering and content-based filtering are two different approaches to building recommendation engines.

Collaborative filtering

Collaborative filtering is based on the idea that people who share similar interests or behavior in the past are likely to share similar interests or behavior in the future. Collaborative filtering uses data on users' interactions with the system, such as their ratings, reviews, or purchase history, to find similarities between users and recommend items that similar users have enjoyed in the past.

For example, let's say you are browsing a streaming service and have watched several action movies in the past. A collaborative filtering recommendation engine might use this information to recommend other action movies that have been enjoyed by other users with similar viewing histories.

Content-based filtering

Content-based filtering, on the other hand, focuses on the attributes of items themselves to make recommendations. It uses data on the content or characteristics of items, such as their genre, plot, or style, to recommend similar items to users.

For example, let's say you are browsing an online bookstore and have purchased several romance novels in the past. A content-based filtering recommendation engine might use this information to recommend other romance novels with similar themes, settings, or writing styles.

Collaborative filtering and content-based filtering both have their strengths and weaknesses, and the choice of which to use will depend on the specific use case and available data. Hybrid approaches that combine both methods can also be effective in certain situations.

Hybrid filtering

Hybrid filtering is a combination of collaborative and content based filtering.

Advantages of a recommendation engine