亚马逊雨林
计算机科学
协同过滤
万维网
推荐系统
生态学
生物
作者
Greg Linden,Brent Smith,Jeremy York
出处
期刊:IEEE Internet Computing
[Institute of Electrical and Electronics Engineers]
日期:2003-01-01
卷期号:7 (1): 76-80
被引量:4472
标识
DOI:10.1109/mic.2003.1167344
摘要
Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.
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