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用户数量以前所未有的速度增长。数字信息量与网络用户数量的指数级增长引发了信息过载问题，阻碍用户快速获取感兴趣的内容。谷歌、DevilFinder与Altavista等信息检索系统虽部分克服了这一挑战，但缺乏信息个性化定制功能（即系统根据用户兴趣与偏好匹配可用信息的能力），导致对推荐系统的需求空前高涨。推荐系统作为信息过滤系统，通过基于用户兴趣、偏好、评分等特征，从海量动态数据中筛选关键信息片段，有效解决信息过载问题。该系统能够根据用户画像判断用户对特定物品的喜好倾向。