Recommendation algorithms form an essential part of every user experience today. The algorithmic processes analyze user interactions and compare them with the millions of interactions of other users for making predictions.
From the viewpoint of logic, the recommendation algorithm aims at reducing the number of options and organizing them. In other words, instead of offering endless amounts of possibilities, the computer sorts out all available items according to relevance.
Machine learning at the core
Machine learning serves as the core of every recommendation algorithm. This way, recommendation algorithms recognize patterns, rather than strictly adhering to pre-programmed rules. For instance, if a user frequently plays upbeat tracks late in the evening, then they will get recommended similar tunes at the same time of day.
Two common approaches
There are two common approaches to recommendation algorithms. Collaborative filtering relies on finding out what similar people found interesting. If some songs, videos, or products gained much attention from students sharing some traits with a particular person, then this individual would receive recommendations related to these items as well. In contrast, content-based filtering is all about finding similar characteristics of items. For example, if a user regularly listens to songs performed by rock stars, then their recommendations will include similar content.
Speed and scale
In addition, every recommendation algorithm must work fast. There is simply no point in comparing each available option to each registered user in real time since there are thousands or even millions of options and millions of users. Therefore, most recommendation algorithms operate in two phases: gathering the list of items to be considered and prioritizing these recommendations.
Learning from interaction
User interaction becomes crucial for learning the recommendation algorithms. Whenever you press play, finish watching a video, switch off the current track, or purchase something, you teach the software to offer something more similar or more interesting to you.
For instance, Spotify uses listening and content information to propose suitable songs, playlists, or artists to its user base. Similarly, Snapchat uses ranking algorithms for its Discover feed but tries to keep its users' streams diverse.
Why it matters
Recommendation algorithms significantly contribute to user experience in terms of making technology more accessible to everyone. They help people save time, avoid dealing with numerous irrelevant options, and find content they might otherwise miss. On the other hand, recommendation algorithms influence the exposure to particular content. That is why every platform is continually improving its recommendation engines.
To sum up, a recommendation algorithm may be described as a pattern-finding device.
Citations
- Recommendation Engines: How They Work — Aerospike
- How Recommendation Algorithms Work — Scientific American
- How We Rank Content on Discover — Snapchat
- How Do Recommender Systems Work on Digital Platforms? — Brookings
- Recommendation System — NVIDIA Glossary
- Discover Weekly: How Spotify Is Changing the Way We Consume Music — Harvard
- How Valuable Are Online Product Recommendations to Consumers? — UF Warrington
