hers. Furthermore, both Eyes and Amazon. com Delivers site in making money by increasing both loyalty, and the number the site to keep a customer aware of the site and of items of re the customer may have missed. Customers appreciate the email recommendations because they help them watch out for new items they are interested in purchasing. These features assist the Business/Applications Recommendation Recommendation Finding Customers who Bought Similar Item Item to Item Correlation Organic Navigation Purchase data ttribute based KEywords/freeform azon. com delivers Attribute Based Selection options Book matcher Top N List People to People Correlation Request List Customer Comments Average Rating Aggregated Rati OrganIc Navigation Text Comments CDNOW Album advise Similar ltem Item to Item Correlation Organic Navigation Top N List Purchase data Keywords/freeform My CDNoW Top N List People to People Correlation Organic Navigation Request list ay Average Rating Aggregated Rating Organic Navigation Text Comments Likert Text Levis tyle Finder Top N list People to People Correlation Request list Moviefinder. com Match Maker Similar item Item to Item Correlation Navigate to an item Editor's choice We predict Top n list people to People Correlatic Keywords/freeform Ordered Search Results Selection options Average Rat Likert anic na REels Movie matches Similar Item Item to Item Correlation Organic Navigation Editor's choice Movie Map Attribute based Editor 's choice customers with recommendations based directly on the text street" is the average rating feature. Rather than comments of other customers. Amazon coms Customer customers to browse a list of text based opinions Comments and eBays Feedback Profile streamlines the customers can provide numerical ranking opinions. B gathering of"the word on the street" by allowing customers to aggregating these rankings into an average rating, Customer locate an item of interest and browse the comments of other Comments and Feedback Profile both provide users with a"one customers. This helps sites make money by providing impartial stop"check on the quality of an item. Similar to text comments, information on the goods/services being sold - the thought being average ratings should facilitate in converting browsers into if enough people claim that a book is good or a seller is credible buyers, and increasing customer loyalty to the an it is likely to be true. This not only helps convert browsers Top-N: Amazon. com's Book Matcher, Levi's Style Finder and into buyers, but should oyalty to a site. If customers My CdNOW, among others, take advantage of recommendations learn they can trust these third party recommendations, than they through a top-N list. Once each site has learned details about a are more likely to return the next time they are faced with a questionable decision. customer's likes and dislikes, each is able to provide thecustomers.. Furthermore, both Eyes and Amazon.com Delivers allows the site to keep a customer aware of the site and of items the customer may have missed. Customers appreciate the email recommendations because they help them watch out for new items they are interested in purchasing. These features assist the site in making money by increasing both loyalty, and the number of return visits. Business/Applications Recommendation Interface Recommendation Technology Finding Recommendations Amazon.com Customers who Bought Similar Item Item to Item Correlation Purchase data Organic Navigation Eyes Email Attribute Based Keywords/freeform Amazon.com Delivers Email Attribute Based Selection options Book Matcher Top N List People to People Correlation Likert Request List Customer Comments Average Rating Text Comments Aggregated Rating Likert Text Organic Navigation CDNOW Album Advisor Similar Item Top N List Item to Item Correlation Purchase data Organic Navigation Keywords/freeform My CDNOW Top N List People to People Correlation Likert Organic Navigation Request List eBay Feedback Profile Average Rating Text Comments Aggregated Rating Likert Text Organic Navigation Levis Style Finder Top N List People to People Correlation Likert Request List Moviefinder.com Match Maker Similar Item Item to Item Correlation Editor’s choice Navigate to an item We Predict Top N List Ordered Search Results Average Rating People to People Correlation Aggregated Rating Likert Keywords/freeform Selection options Organic Navigation Reel.com Movie Matches Similar Item Item to Item Correlation Editor’s choice Organic Navigation Movie Map Browsing Attribute Based Editor’s choice Keywords/freeform Table 1: Recommender System Examples Text Comments: More and more frequently, sites are providing customers with recommendations based directly on the text comments of other customers. Amazon.com’s Customer Comments and eBay’s Feedback Profile streamlines the gathering of “the word on the street” by allowing customers to locate an item of interest and browse the comments of other customers. This helps sites make money by providing impartial information on the goods/services being sold – the thought being, if enough people claim that a book is good, or a seller is credible, than it is likely to be true. This not only helps convert browsers into buyers, but should increase loyalty to a site. If customers learn they can trust these third party recommendations, than they are more likely to return the next time they are faced with a questionable decision.. Average Rating: Even simpler access to “the word on the street” is the average rating feature. Rather than asking customers to browse a list of text based opinions, other customers can provide numerical ranking opinions. By aggregating these rankings into an average rating, Customer Comments and Feedback Profile both provide users with a “one stop” check on the quality of an item. Similar to text comments, average ratings should facilitate in converting browsers into buyers, and increasing customer loyalty to the site. Top-N: Amazon.com’s Book Matcher, Levi’s Style Finder and My CDNOW, among others, take advantage of recommendations through a top-N list. Once each site has learned details about a customer’s likes and dislikes, each is able to provide the