An Overview of Algorithmic Recommendation Systems

There are three main types of algorithms that can be used in recommendation systems:

1. Content-based Recommender Systems: Content-based recommender systems operate by suggesting items to a user that are similar in attributes to items that a user has previously demonstrated interest in.¹ Content-based recommender systems evaluate the attributes of an item, such as its metadata (e.g. tags or text).² Although different recommendation systems can vary in their exact composition, most content-based recommender systems operate by creating a profile of a user that outlines their interests and preferences, such as alternative rock music or drug store makeup. The system will then compare this user profile to existing items in its database in order to identify a match.³ A user’s profile is based on explicit and implicit feedback that a user provides on recommendations as a way to indicate their preferences and interests further.⁴ For example, a user may explicitly indicate preferences by rating a particular item, or implicitly by clicking on certain suggested items and not others. The algorithm uses this information to categorize these preferences and refine recommendations going forward. Unlike other systems discussed below, content-based recommender systems do not typically account for the preferences or actions of other users when making recommendations. Rather, their recommendations are primarily based on a given user’s past interactions.⁵

2. Collaborative-filtering Recommender Systems: Collaborative-filtering recommender systems operate by suggesting items to a user based on the interests and behaviors of other users who are identified as having similar preferences or tastes.⁶ The process is considered collaborative because these recommender systems make automated predictions (known as filtering) about a user’s interests and preferences based on data about other user’s who have similar preferences and interests.⁷ Collaborative-filtering recommender systems make these determinations by mining user behavior, such as purchase records and user ratings,⁸ and through techniques like pattern recognition.⁹

Collaborative-filtering recommender systems are considered particularly useful because they allow comparison and ranking of completely different types of items. This is because these algorithms do not need to know about the attributes of an item. Rather, they only need to know which items are bought together.¹⁰ In addition, because collaborative-filtering recommender systems make recommendations based on a vast dataset of other user behaviors and preferences, researchers have found that this approach yields more accurate results compared to other techniques. For example, keyword searching offers a more narrow assessment of datasets.¹¹ Keyword searching is typically deployed during behavioral targeting, which is when advertisers use information on a user’s browsing history and behavior to customize the ad targeting and delivery process.¹²

In addition, some researchers consider collaborative-filtering recommender systems to be able to deliver more accurate results for users with both mainstream and niche interests when compared to the other types of recommender systems outlined in this report. This is because a programmer can control how many users in the database should be considered as part of the data set for calculating recommendations; as a result, the programmer can optimize the algorithm to balance recommending popular and niche results.¹³ According to researchers, collaborative-filtering recommender systems are modeled after the ways individuals solicit feedback and recommendations from their social circle offline.¹⁴ Such recommender systems seek to automate this process based on the notion that if two similar individuals like an item or piece of content, there are also likely several other items that they would both find interesting.¹⁵

Collaborative-filtering recommender systems rely on two primary types of algorithms: user-based and item-based collaborative-filtering algorithms. Both types rely on users’ ratings on items. In the user-based category of algorithms, the algorithm scores an item a user has not rated by combining the ratings of similar users.¹⁶ In the item-based approach, the algorithm matches a user’s purchased and rated items with similar items, and then combines these similar items into a list of recommended products for the initial user.¹⁷

3. Knowledge-based Recommender Systems: Knowledge-based recommender systems make suggestions based on the attributes of a user and an item. These systems typically rely on data-mining methods and advanced natural language processing (NLP) to identify and evaluate an item’s attributes (e.g. price or technical specifications such as HD or BluRay functionality). The system then identifies similarities between Item A’s attributes and User A’s preferences (e.g. preference for high-end equipment), and makes recommendations based on its findings. For example, such a system could identify attribute similarities between a job seeker’s resume and a job description. This recommender system does not typically consider a user’s past behaviors, and it is therefore considered most effective when engaging with a new user or a new item.¹⁸

Most recommendation systems deploy a hybrid of these three filter categories.¹⁹ Traditionally, both content-based and collaborative-filtering recommender systems rely on explicit input from a user. For example, algorithms can collect user ratings, likes, or reactions to an item or piece of content. These systems can also use implicit user input, which is drawn from data on user activities and behaviors.²⁰ These can include a user’s clicks, search queries, and purchase history, as well as a user adding an item to their cart, completing a purchase, and reading an entire article.²¹ Using both of these data types, these systems can be refined so that they deliver more personalized recommendations.