In recent years, the amount of data available on the internet and the number of users who utilize the Internet have increased at an unparalleled pace. The exponential development in the quantity of digital information accessible and the number of Internet users has created the possibility for information overload, impeding fast access to items of interest on the Internet. Information retrieval systems like as Google, DevilFinder, and Altavista have partly overcome this challenge, but prioritizing and customization of information (where a system maps accessible material to a user's interests and preferences) were lacking. This has resulted in a higher-than-ever need for recommender systems. Recommender systems are information filtering systems that address the issue of information overload by filtering important information fragments from a huge volume of dynamically produced data based on the user's interests, favorite things, preferences and ratings on the desired item. Recommender systems can figure out if a person would like an item or not based on their profile.

翻译：近年来，互联网上可获取的数据量及使用网络的用户数量以前所未有的速度增长。数字信息可用量及互联网用户数量的指数级增长引发信息过载问题，阻碍了用户快速获取感兴趣的内容。谷歌、DevilFinder和Altavista等信息检索系统已部分克服这一挑战，但仍缺乏信息的优先级排序与个性化定制能力（即系统将可用内容与用户兴趣及偏好进行匹配的功能）。这使得推荐系统的需求空前高涨。推荐系统作为信息过滤系统，通过根据用户兴趣、偏好、评分及对目标项目的喜好程度，从海量动态数据中筛选重要信息片段，有效应对信息过载问题。推荐系统能够基于用户画像判断用户是否可能喜欢某个项目。