portant in both Reel. co Movie Matches/Map ar systems in E-commerce sites. These range from simple Moviefinder com's match Ma variations on existing systems, to entirely new types of systems 5. Finding recommendations As discussed above, many sites currently use purchase data as an Just as sites can utilize different methods for calculating implicit, positive rating. CDNOW has realized in My CDNow displaying recommendations, so can they utilize different that owning something cannot always be interpreted as a methods for allowing customers to access the recommendations positive. Recall that CDNOW allows customers to later go back Through our recommender system examples we have identified and indicate "own it but dislike it". However. few sites are four different methods for ons ea attempting to extract implicit negative ratings from purchase interface and/or technology. These four methods are orderdi which may provide access to more than one recommendatio data. One way to do this would be through the analysis of data on returned products. While customers may return an item for a the amount of customer effort required to find the variety of reasons, in general any return could be considered as a negative rating on the item in question. Another model of mplicit negative rating can be derived from detail views. If the Organic Navigation: Requiring the least amount of work to site presents a few products in low detail and the customer actually access recommendations is the organic navigation chooses to view some products at higher detail, but ignores process. In applications such as Album Advisor, Movie Matches, others, a mild negative rating can be inferred for the unselected and Feedback Profile, customers do nothing extra in order to items. Many recommender system algorithms perform better receive recommendations. In each of these applications with both negative and positive ratings, so the negative data can recommendations appear as part of the item information page. be These recommendations can consist of additional items to consider, average ratings, or a list of other customer comments Another creative use of a recommender system would be to use it normal navigation of the site, customers are provided wIth.% However, the underlying similarity is that through the course in reverse to explain to a user what type of thing a product is For instance, a recommender system might be used to tell the user this product you're looking at is similar to these other products that you have liked in the past". Recommender system Request Recommendation List: Requiring not much more algorithms that correlate items can be used in this way. For best work from the customer is the request recommendation list results they should be modified to return items that the user has process. Customers using applications such as Book Matcher purchased in the past, rather than the usual set of items the user and Style Finder can access recommendations based on their has not purchased in the past previously recorded likes/dislikes. To do so, they simply have to equest these recommendations from the system Current recommender systems only use a small subset of th available information about the customer in making their Selection Options: In the selection options process customers recommendations. Some systems use demographic information, must truly interact with the system in order to receive some use purchase data information, some use explicit ratings, recommendations. Typically, customers choose from a set of some use ownership data, but no system effectively uses all thi redefined criterion/options up which to base their data simultaneously for real-time recommendations. How should recommendations. For example, users of Amazon. com Delivers these diverse types of data be combined? Should individual ave a choice from nearly 50 pre-defined categories in which to recommender systems running on each type of data produce receive periodic recommendations. Even more involved, users of independent recommendations? Or can better recommendations Moviefinder coms We Predict system can select from a finite be produced by using all of the available data simultaneousl list of title, format, length and genre options to define a search, Recommender system algorithms that use many different types of s well as customizing options such as ranking method and display features data create the possibility for"subtle personalization, in whicl the site provides a completely organic personalized experience to Keyword/Freeform: Arguably, the keyword freeform the customer. The customer interacts with the site just as she requires the most interaction from the customer. In applica would have before personalization. She does not need to take any ch as Eyes, customers provide a set of textual keywords upon explicit actions to inform the site of her interests or desires. The which to retrieve future recommendations. A version of Album site subtly changes the interface in nearly invisible ways to create Advisor takes the freeform input of multiple artists upon which a more personal experience for her, without her even noticing make recommendation matches. The We Predict and Movie that anything has changed!(Balabanovic Shoham, 1997) Map applications produce recommendations from the results of a(Basu, Hirsh, Cohen, 1998)(Sarwar et al, 1998) query conducted using the keywords provided. While each uses Recommender systems are currently used as virtual salespeople, the keywords in very different manners, each requires the user to rather than as marketing tools. The difference is that many know specifically what types of things they are interested in. recommender systems target each individual customer 6. E-Commerce Opportunities differently, making it difficult to produce the reports that have already explored multiple interfaces, technologies, ir Many varieties of recommender systems are already in use. We marketing professionals are used to. These reports usual partition the population into a manageable number of segments information needs for these types of systems. However, there One way to bring these two worlds together would be to use th remain many opportunities for the expansion of recommender people to people correlations used by some recommender system algorithms to create segments for the reports. Open questionsimportant in both Reel.com’s Movie Matches/Map and Moviefinder.com’s Match Maker. 5. Finding Recommendations Just as sites can utilize different methods for calculating or displaying recommendations, so can they utilize different methods for allowing customers to access the recommendations. Through our recommender system examples we have identified four different methods for finding recommendations each of which may provide access to more than one recommendation interface and/or technology. These four methods are ordered in the amount of customer effort required to find the recommendations. Organic Navigation: Requiring the least amount of work to actually access recommendations is the organic navigation process. In applications such as Album Advisor, Movie Matches, and Feedback Profile, customers do nothing extra in order to receive recommendations. In each of these applications, recommendations appear as part of the item information page. These recommendations can consist of additional items to consider, average ratings, or a list of other customer comments. However, the underlying similarity is that through the course of normal navigation of the site, customers are provided with a recommendations. Request Recommendation List: Requiring not much more work from the customer is the request recommendation list process. Customers using applications such as Book Matcher and Style Finder can access recommendations based on their previously recorded likes/dislikes. To do so, they simply have to request these recommendations from the system. Selection Options: In the selection options process customers must truly interact with the system in order to receive recommendations. Typically, customers choose from a set of predefined criterion/options upon which to base their recommendations. For example, users of Amazon.com Delivers have a choice from nearly 50 pre-defined categories in which to receive periodic recommendations. Even more involved, users of Moviefinder.com’s We Predict system can select from a finite list of title, format, length and genre options to define a search, as well as customizing options such as ranking method and display features. Keyword/Freeform: Arguably, the keyword/freeform option requires the most interaction from the customer. In applications such as Eyes, customers provide a set of textual keywords upon which to retrieve future recommendations. A version of Album Advisor takes the freeform input of multiple artists upon which to make recommendation matches. The We Predict and Movie Map applications produce recommendations from the results of a query conducted using the keywords provided. While each uses the keywords in very different manners, each requires the user to know specifically what types of things they are interested in. 6. E-Commerce Opportunities Many varieties of recommender systems are already in use. We have already explored multiple interfaces, technologies, and information needs for these types of systems. However, there remain many opportunities for the expansion of recommender systems in E-commerce sites. These range from simple variations on existing systems, to entirely new types of systems. As discussed above, many sites currently use purchase data as an implicit, positive rating. CDNOW has realized in My CDNOW that owning something cannot always be interpreted as a positive. Recall that CDNOW allows customers to later go back and indicate “own it but dislike it”. However, few sites are attempting to extract implicit negative ratings from purchase data. One way to do this would be through the analysis of data on returned products. While customers may return an item for a variety of reasons, in general any return could be considered as a negative rating on the item in question. Another model of implicit negative rating can be derived from detail views. If the site presents a few products in low detail and the customer chooses to view some products at higher detail, but ignores others, a mild negative rating can be inferred for the unselected items. Many recommender system algorithms perform better with both negative and positive ratings, so the negative data can be valuable. Another creative use of a recommender system would be to use it in reverse to explain to a user what type of thing a product is. For instance, a recommender system might be used to tell the user “this product you’re looking at is similar to these other products that you have liked in the past”. Recommender system algorithms that correlate items can be used in this way. For best results they should be modified to return items that the user has purchased in the past, rather than the usual set of items the user has not purchased in the past. Current recommender systems only use a small subset of the available information about the customer in making their recommendations. Some systems use demographic information, some use purchase data information, some use explicit ratings, some use ownership data, but no system effectively uses all this data simultaneously for real-time recommendations. How should these diverse types of data be combined? Should individual recommender systems running on each type of data produce independent recommendations? Or can better recommendations be produced by using all of the available data simultaneously? Recommender system algorithms that use many different types of data create the possibility for “subtle personalization”, in which the site provides a completely organic personalized experience to the customer. The customer interacts with the site just as she would have before personalization. She does not need to take any explicit actions to inform the site of her interests or desires. The site subtly changes the interface in nearly invisible ways to create a more personal experience for her, without her even noticing that anything has changed! (Balabanovic & Shoham, 1997) (Basu, Hirsh, & Cohen, 1998) (Sarwar et al, 1998) Recommender systems are currently used as virtual salespeople, rather than as marketing tools. The difference is that many recommender systems target each individual customer differently, making it difficult to produce the reports that marketing professionals are used to. These reports usually partition the population into a manageable number of segments. One way to bring these two worlds together would be to use the people to people correlations used by some recommender system algorithms to create segments for the reports. Open questions include “how can names be assigned to the automatically