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A recommender system, also called a recommendation algorithm, recommendation engine, recommendation platform, or in the context of social media, simply algorithm is a type of information filtering system that suggests items most relevant to a particular user.

The value of these systems becomes particularly evident in scenarios where users must select from a large number of options, such as products, media, or content. Major social media platforms and streaming services rely on recommender systems that employ machine learning to analyze user behavior and preferences, thereby enabling personalized content feeds. The implementation of recommender systems is pervasive, with commonly recognised examples including the generation of playlist for video and music services, the provision of product recommendations for e-commerce platforms, and the recommendation of content on social media platforms and the open web. These systems can operate using a single type of input, such as music, or multiple inputs from diverse platforms, including news, books and search queries. Additionally, popular recommender systems have been developed for specific topics, such as restaurants and online dating services. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services. A content discovery platform is a software recommendation platform that employs recommender system tools.

Recommender systems usually make use of either or both collaborative filtering and content-based filtering, as well as other systems such as knowledge-based systems.

The differences between collaborative and content-based filtering can be demonstrated by comparing two early music recommender systems, Last.fm and Pandora Radio.

Elaine Rich created the first recommender system in 1979, called Grundy.

The system generates recommendations using only information about rating profiles for different users or items. Many algorithms have been used in measuring user similarity or item similarity in recommender systems.

A history of the user's interaction with the recommender system. When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended. To overcome this, most content-based recommender systems now use some form of the hybrid system. Content-based recommender systems can also include opinion-based recommender systems. These user-generated texts are implicit data for the recommender system because they are potentially rich resources of both feature/aspects of the item and users' evaluation/sentiment to the item. Popular approaches of opinion-based recommender system utilize various techniques including text mining, information retrieval, sentiment analysis (see also Multimodal sentiment analysis) and deep learning.

Most recommender systems now use a hybrid approach, combining collaborative filtering, content-based filtering, and other approaches.

These recommender systems use the interactions of a user within a session to generate recommendations.

Multi-criteria recommender systems (MCRS) can be defined as recommender systems that incorporate preference information upon multiple criteria.

In A/B tests, recommendations are shown to typically thousands of users of a real product, and the recommender system randomly picks at least two different recommendation approaches to generate recommendations.

Trust – A recommender system is of little value for a user if the user does not trust the system.

Recommender systems are notoriously difficult to evaluate offline, with some researchers claiming that this has led to a reproducibility crisis in recommender systems publications. Deep learning and neural methods for recommender systems have been used in the winning solutions in several recent recommender system challenges, WSDM, RecSys Challenge. Hence, operators of recommender systems find little guidance in the current research for answering the question, which recommendation approaches to use in a recommender systems.

The AI-based recommender can analyze complex data sets, learning from user behavior, preferences, and interactions to generate highly accurate and personalized content or product suggestions.

Collaborative filtering (CF) is one of the most commonly used recommendation system algorithms. The system will make recommendations based on that similarity

Quick Facts

• A content discovery platform is a software recommendation platform that employs recommender system tools.
• Trust – A recommender system is of little value for a user if the user does not trust the system.
• Recommender systems usually make use of either or both collaborative filtering and content-based filtering, as well as other systems such as knowledge-based systems.
• Major social media platforms and streaming services rely on recommender systems that employ machine learning to analyze user behavior and preferences, thereby enabling personalized content feeds.
• Deep learning and neural methods for recommender systems have been used in the winning solutions in several recent recommender system challenges, WSDM, RecSys Challenge.

Source material: Wikipedia - "Recommender system". Adapted and summarized for DiscoverScroll. Original contributors are credited through the linked Wikipedia article. Read original on Wikipedia. CC BY-SA 4.0. Changes were made from the original.