A Brief History of Recommender Systems

TL;DR

This paper provides a concise historical overview of web recommender systems, highlighting their evolution in models and architectures, and emphasizing their impact on transforming big data into valuable personalized content.

Contribution

It offers a summarized review of the development of recommender systems, connecting past progress to future innovations in recommendation technology.

Findings

Recommender systems have become essential web applications serving billions daily.

They have evolved significantly in models and architectures over time.

Recommender systems effectively convert big data into personalized content.

Abstract

Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry. Currently, recommender system has become one of the most successful web applications, serving billions of people in each day through recommending different kinds of contents, including news feeds, videos, e-commerce products, music, movies, books, games, friends, jobs etc. These successful stories have proved that recommender system can transfer big data to high values. This article briefly reviews the history of web recommender systems, mainly from two aspects: (1) recommendation models, (2) architectures of typical recommender systems. We hope the brief review can help us to know the dots about the progress of web recommender systems, and the dots will somehow connect in the future, which inspires us to build more advanced recommendation services for changing the world better.