2. 4 Levis describes the recommendation technology used by the site, and Style Finder: Style Finder allows customers of the Levi the inputs required by that technology. The fourth column describes how users find recommendations using the application on articles of Levis clothing. Customers indicate whether they Each of the columns of Table I is the subject of one of the are male or female, then view three categories- Music, Looks Fun-and rate a minimum of 4"terms"or"sub-categories the table, and their role in supporting recommender systems for within each. They do this by providing a rating on a 7-point cale ranging trom leave it to "love it. They may also choose 3. Recommendation Interfaces and Ways to are entered customers may select "get recommendations."Here, Make Money they are provided with thumbnails of 6 items of recommended An old proverb states that there is "more than one way to skin a clothing. Customers may provide feedback by use of the"tell us cat. One would assume that the method selected depends on the what you think feature"which allows them to enter an opinion desired outcome. Similarly, there is more than one way to rating for the recommended article of clothing. Feedback may display recommendations to a customer. The method selected change one or all of the six items recommended may well depend on how the e-commerce site wants the customer to use the recommendation. In the following we will examine 2.5 Moviefinder. com seven recommendation interfaces and how each assists the site Match Maker: Moviefinder coms Match in making money. While some of these methods have their roots (www.moviefinder.com)allowscustomerstolocatemovies in traditional commerce, each of them draws upon the strengths a similar"mood, theme, genre or cast" to a given movie. From of the electronic medium to provide more powerful the information page of the movie in question, customers click on recommendations the Match Maker icon and are provided with the list of Browsing: In traditional commerce a customer might walk into a recommended movies, as well as links to other films by the video store and ask the clerk to recommend "a comedy from the original films director and key actors 50s. Ideally, the clerk would recommend several movies, and We Predict: We Predict recommends movies to customers the customer could go off to locate the recommended movies based on their previously indicated interests. Customers enter a browse the box covers, and see which ones appealed to them. rating on a 5-point scale- from a to F- for movies they have However, the quality of the recommendations provided was viewed. These ratings are used in two different ways. Most dependent on the particular clerk's knowledge of an enormous simply, as they continue, the information page for non-rated range of movies. Reel. com has several advantages when movies contains a personalized textual prediction(go see it implementing browsing into their Movie Map feature. First, the forget it). In a variation of this, customers can use Powerfind to recommendations of several clerks/editors can be combined so search for top picks based on syntactic criteria such as Genre, that higher quality recommendations can be provided no matter directors, or actors and choose to have these sorted by their what the query parameters. Furthermore, recommendations are personalized prediction or by the all customer average returmed with immediate links to the items being recommended no more searching the store for the obscure videos recommended 2. 6 Reel. com Recommended browsing helps the E-commerce lovie Matches: Similar to Amazon. com's Custe BoughtReel.comsMovieMatches(www.reel.com)providesnarrowdowntheirchoicesandfeelmoreconfident recommendations on the information page for each movie. These decision to buy by providing organized access to the recommendations consist of"close matches"and/or "creative recommendations matches."Each set consists of up to a dozen hyperlinks to the Similar Item: Another modification of traditional commerce information pages for each of these"matched"films. The hyperlinks are annotated with one sentence descriptions of how techniques is the similar item recommendation. Systems such as the new movie is similar to the original movie in question Reel coms Movie Matcher. Amazon coms Customer's whe ("Darker thriller raises similarly disturbing questions. " ) Bought and one variation of CDNOW's Album Advisor attempt to expose customers to items they may have forgotten about, or of Movie Map: The Movie Map feature of Reel. com recommends which they may have simply been unaware to customers based on syntactic features. Customers implementation in E-commerce sites allows for more specific and queries based on Genre, movie types, viewing format personalized recommendations. The items displayed can be prices, and request results be constrained to"sleepers"or entirely selected based on the item(s)in which a customer has best of this genre. The recommendations s are editor's shown interest. In doing so, sites increase customer's exposure recommendations for movies that fit the specified criteria. to their product line, and ideally are able to sell more items per 2.7 Summary Email: Recommendations can also be delivered directly to recommendation hnology, and how users find customers through email, in a extension of traditional direct mail recommendations for all of the example applications. The first Amazon coms Eyes feature allows them to notit column just names each application, under the E-commerce site customers the minute an item becomes commercially available that houses it The second column describes the interface that Eyes enables Amazon. com to attract customers into their store used for delivering the recommendations. The third column before other stores with the same product can reach those2.4 Levis Style Finder: Style Finder allows customers of the Levi Straus™ (www.levis.com) website to receive recommendations on articles of Levi’s clothing. Customers indicate whether they are male or female, then view three categories -- Music, Looks, Fun -- and rate a minimum of 4 “terms” or “sub-categories” within each. They do this by providing a rating on a 7-point scale ranging from “leave it” to “love it.” They may also choose the rating of “no opinion.” Once the minimum number of ratings are entered customers may select “get recommendations.” Here, they are provided with thumbnails of 6 items of recommended clothing. Customers may provide feedback by use of the “tell us what you think feature” which allows them to enter an opinion rating for the recommended article of clothing. Feedback may change one or all of the six items recommended. 2.5 Moviefinder.com Match Maker: Moviefinder.com’s Match Maker (www.moviefinder.com) allows customers to locate movies with a similar “mood, theme, genre or cast” to a given movie. From the information page of the movie in question, customers click on the Match Maker icon and are provided with the list of recommended movies, as well as links to other films by the original film’s director and key actors. We Predict: We Predict recommends movies to customers based on their previously indicated interests. Customers enter a rating on a 5-point scale -- from A to F – for movies they have viewed. These ratings are used in two different ways. Most simply, as they continue, the information page for non-rated movies contains a personalized textual prediction (go see it – forget it). In a variation of this, customers can use Powerfind to search for top picks based on syntactic criteria such as Genre, directors, or actors and choose to have these sorted by their personalized prediction or by the all customer average. 2.6 Reel.com Movie Matches: Similar to Amazon.com’s Customers who Bought, Reel.com’s Movie Matches (www.reel.com) provides recommendations on the information page for each movie. These recommendations consist of “close matches” and/or “creative matches.” Each set consists of up to a dozen hyperlinks to the information pages for each of these “matched” films. The hyperlinks are annotated with one sentence descriptions of how the new movie is similar to the original movie in question (“Darker thriller raises similarly disturbing questions… ”). Movie Map: The Movie Map feature of Reel.com recommends movies to customers based on syntactic features. Customers enter queries based on Genre, movie types, viewing format and/or prices, and request results be constrained to “sleepers” or “best of this genre.” The recommendations are editor’s recommendations for movies that fit the specified criteria. 2.7 Summary In Table 1 we have summarized the applications, interfaces, recommendation technology, and how users find recommendations for all of the example applications. The first column just names each application, under the E-commerce site that houses it. The second column describes the interface that is used for delivering the recommendations. The third column describes the recommendation technology used by the site, and the inputs required by that technology. The fourth column describes how users find recommendations using the application. Each of the columns of Table 1 is the subject of one of the sections of this paper, describing the meaning of the entries in the table, and their role in supporting recommender systems for E-commerce. 3. Recommendation Interfaces and Ways to Make Money An old proverb states that there is “more than one way to skin a cat.” One would assume that the method selected depends on the desired outcome. Similarly, there is more than one way to display recommendations to a customer. The method selected may well depend on how the e-commerce site wants the customer to use the recommendation. In the following we will examine seven recommendation interfaces, and how each assists the site in making money. While some of these methods have their roots in traditional commerce, each of them draws upon the strengths of the electronic medium to provide more powerful recommendations. Browsing: In traditional commerce a customer might walk into a video store and ask the clerk to recommend “a comedy from the 50s.” Ideally, the clerk would recommend several movies, and the customer could go off to locate the recommended movies, browse the box covers, and see which ones appealed to them. However, the quality of the recommendations provided was dependent on the particular clerk’s knowledge of an enormous range of movies. Reel.com has several advantages when implementing browsing into their Movie Map feature. First, the recommendations of several clerks/editors can be combined so that higher quality recommendations can be provided no matter what the query parameters. Furthermore, recommendations are returned with immediate links to the items being recommended – no more searching the store for the obscure videos recommended. Recommended browsing helps the E-commerce site by converting browsers into buyers. It does so by helping the users narrow down their choices and feel more confident in their decision to buy by providing organized access to the recommendations. Similar Item: Another modification of traditional commerce techniques is the similar item recommendation. Systems such as Reel.com’s Movie Matcher, Amazon.com’s Customer’s who Bought and one variation of CDNOW’s Album Advisor attempt to expose customers to items they may have forgotten about, or of which they may have simply been unaware. Their implementation in E-commerce sites allows for more specific and personalized recommendations. The items displayed can be entirely selected based on the item(s) in which a customer has shown interest. In doing so, sites increase customer’s exposure to their product line, and ideally are able to sell more items per order. Email: Recommendations can also be delivered directly to customers through email, in a extension of traditional direct mail techniques. Amazon.com’s Eyes feature allows them to notify customers the minute an item becomes commercially available. Eyes enables Amazon.com to attract customers into their store before other stores with the same product can reach those