Recommender systems use algorithms to provide users with product or service
recommendations. Recently, these systems have been using machine learning algorithms …

The ongoing rapid expansion of the Internet greatly increases the necessity of effective
recommender systems for filtering the abundant information. Extensive research for …

推荐系统是电子商务系统中最重要的技术之一. 随着电子商务系统用户数目和商品数目的日益
增加, 在整个商品空间上用户评分数据极端稀疏, 传统的相似性度量方法均存在各自的弊端 …

对个性化服务技术中用户描述文件的表达与更新, 资源描述文件的表达, 个性化推荐技术,
个性化服务体系结构以及该领域的主要研究成果进行了综述. 通过比较现有原型系统的实现方式 …

对现有的推荐系统评价指标进行了系统的回顾, 总结了推荐系统评价指标的最新研究进展,
从准确度, 多样性, 新颖性及覆盖率等方面进行多角度阐述, 并对各自的优缺点以及适用环境进行 …

Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …

E-business leverages digital channels to scale its functions and services and operates by
connecting and retaining customers using marketing initiatives. To increase the likelihood of …

Due to the explosion of available information on the Internet, the need for effective means of
accessing and processing them has become vital for everyone. Recommender systems …

Recommender systems are an important part of the information and e-commerce ecosystem.
They represent a powerful method for enabling users to filter through large information and …

Since their introduction in the early 1990's, automated recommender systems have
revolutionized the marketing and delivery of commerce and content by providing …