Amazon.com Recommendations: Item-to-Item Collaborative Filtering:
Amazon.com Recommendations: Item-to-Item Collaborative Filtering: "By comparing similar items rather than similar customers, item-to-item collaborative filtering scales to very large data sets and produces high-quality recommendations.
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The click-through and conversion rates [of recommendations based on collabirative filtering] two important measures of Web-based and email advertising effectivenessvastly exceed those of untargeted content such as banner advertisements and top-seller lists.
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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 realtime, scales to massive data sets, and generates high-quality recommendations.
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Rather than matching the user to similar customers, item-to-item collaborative filtering matches each of the user's purchased and rated items to similar items, then combines those similar items into a recommendation list."