Abstract: Improved user interfaces and methods are provided for presenting item recommendations to a user when the user selects an item to add to a shopping cart. In response to the user's selection, a page generation process generates and returns a page that includes a plurality of recommendations sections, each of which displays item recommendations generated using a different respective recommendation or selection algorithm (e.g., recommendations based on shopping cart contents, recommendations based on purchase history, etc.). These recommendation algorithms may be selected based on collected information regarding the user. The page may also include a shopping cart section which displays contents of the shopping cart.

Abstract: A computing system for searching for and identifying data repository deficits includes an interactive database of items, a data repository that stores a first type of information related to items, a search engine configured to identify items that do not have an amount of the first type of information that meets a first threshold, a candidate selection interface that provides functionality for the user to select an item from recommended candidate items needing the first type of information, a user interface via which the user can provide the first type of information for an item selected by the user, a game system configured to calculate for a game scores and rankings based at least in part on data stored in the data repository.

Abstract: Similarities are discovered among different users with respect to their media experiences and other behaviors, such as taste in media items (e.g., books, music, movies, magazines, art, etc.), browsing behavior, purchase decisions, and online shopping habits, and usage history. The similarities are determined in part by substantially real-time comparison of individual users with a set of predetermined user-based clusters formed from the experiences and behaviors of sample users. Users from a population may then be identified based on similarity metrics. Recommendations for new media items, or other goods/services, may also be made based on choices being made by similar users.

Abstract: Technologies are described herein for providing recurring delivery of products to a customer on a periodic basis. A customer creates a recurring delivery list that specifies a particular delivery slot indicating when orders are to be delivered. The customer adds products to the recurring delivery list, specifying a quantity and frequency of delivery for each product. Orders are periodically generated from the customer's recurring delivery list for delivery on the next occurrence of the specified delivery slot. The products included in each order are based on the frequency of delivery and a last delivery date for each product in the recurring delivery list. If the customer makes changes to the recurring delivery list, those changes may also be applied to any pending orders generated from the list.

Abstract: Disclosed are various embodiments for generating item recommendations based on broadcast media consumed by a user on a client device. An item recommendation application receives a quantity of media information associated with the broadcast media being consumed from the client device. The item recommendation application determines a pattern of media consumption behavior for the user based at least in part on an analysis of the user's consumption history. The item recommendation identifies other consumers of broadcast media that exhibit a substantially similar pattern of media consumption behavior. The item recommendation application then gathers items purchased by the other consumers and provides those items as the item recommendations to the requesting process.

Abstract: Behavior-based associations, such as item-to-item or query-to-item associations, are extrapolated to other items to create new associations. The items to which the associations are extrapolated may be “behavior deficient” items, or items for which the quantity of collected user activity data is insufficient to create meaningful or reliable behavior-based associations. The behavior-based associations are extrapolated based on content-based associations, or another type of “substitutability” association, between items. The items can be any type of item (e.g., products, web sites, documents, etc.) for which user behaviors (e.g., purchases, accesses, downloads, etc.) can be monitored and analyzed to detect behavior-based associations, and for which item content or other available information can be used to assess item substitutability.

Abstract: A system can compare a newer model of a product to an older model of a product to determine attributes or features that are new to the newer model of the product. The system can then identify other products in the same product category as the newer model of the product that includes the new attribute or product feature. The system can recommend one or more of the products with the new product feature to a potential customer. In some cases, the system may identify an average upgrade time interval for upgrading the product. Then, if the system determines that the upgrade time interval has elapsed since a user obtained the older model of the product, the system can recommend the newer model of the product and/or other products that include the new product feature to the user.

Abstract: A system comprises: (a) storage and distribution means; (b) inventory upload means; (c) product information download means; (d) order upload means; and (e) delivery download means. A method comprises: (a) digitally transferring the goods and pertinent product information from a provider of the goods to a storage and distribution means; (b) storing the goods, pertinent product information, order information, sales information, or marketing information on the storage and distribution means; (c) digitally transferring product information to a shopper; (d) digitally transferring order information from a buyer of goods to the storage and distribution means; and (e) digitally transferring the goods to the buyer. The storage and distribution means may be provided with a network connection for access by providers, shoppers, and buyers.

Abstract: Behavior-based associations are extrapolated to items for which the quantity of collected user activity data is insufficient to create meaningful or reliable behavior-based associations (“behavior-deficient” items). The behavior-based associations are extrapolated based on content-based associations, or another type of “substitutability” association, between the behavior-deficient items and other items. The items can be any type of item for which user behaviors (e.g., purchases, accesses, downloads, etc.) can be monitored and analyzed to detect behavior-based associations, and for which item content or other available information can be used to assess item substitutability. For example, the items can be products represented in an electronic catalog, web pages or other documents accessible on a network, or web sites.

Abstract: A service is disclosed for enabling web sites and other entities to provide behavior-based content to end users. The service can be implemented as a web service that is remotely accessible over the Internet. Web sites use the web service's interface to report events descriptive of item-related actions performed by end users (e.g., item views, item purchases, searches for items, etc.). The web service analyzes the reported event data on an aggregated basis to detect various types of associations, including associations between particular search queries and items. The detected associations are recorded in site-specific datasets, and are made available to the corresponding sites via the service's interface.

Abstract: A computer-implemented service recommends digital works (and/or creators of works) to a user based on works currently or previously played or downloaded by the user on a player device or based on playlists stored on the player device. The works may be, for example, music files, video files, electronic books, or other digital content for playing by users. A user may thus obtain personalized recommendations that are based on works obtained from sources (web sites, physical CDs, etc.) that are independent of the recommendations system. In one embodiment, the service identifies pairs of works (and/or work creators) that are similar to each other by virtue of the relatively high frequency with which they co-occur on playlists or within play histories of users. The resulting mappings are used to provide recommendations to users.

Abstract: An online merchant can use a product review to customize a solicitation of a product suggestion. If a customer reviews a product unfavorably, the customer might be solicited to suggest alternate products to the reviewed product. If a customer reviews a product favorably, the customer might be solicited to suggest accessories or other products related to the reviewed product. The products suggested by the customer might then be utilized to recommend the suggested products to other customers. Information provided by a customer in a product review or other data might also be utilized to customize a product recommendation for the customer. If a customer reviews a product unfavorably, a recommendation might be provided to the customer for an alternate, or substitutable, product for the reviewed product. If a customer reviews a product favorably, a recommendation might be provided to the customer for related products or accessories to the reviewed product.

Abstract: Disclosed herein is a platform for automating the discovery of product bundles that are promising for sale to customers (i.e., they are relevant, valuable and/or timely). Techniques are disclosed for generating a set of potential product bundles according to criteria, thresholds and/or scores which indicate a propensity for items to be purchased together by customers. The generated set of potential product bundles may be ranked according to opportunity metrics and/or scores which indicate value to a merchant and/or customers. A subset of product bundles is selected from the set of potential product bundles based at least in part on the ranking, and each product bundle in the selected subset is matched to one or more entities, such as merchants. These product bundles may then be presented to the associated (i.e., matched) entity based on some criteria, such as offerings of the merchants.

Abstract: Disclosed are various embodiments for facilitating gift campaigns in association with a social networking system. A computing device may identify a user of a social networking system that is associated with a recipient for a gift campaign. An invitation for the user to participate in the gift campaign can be transmitted by the computing device. The computing device may identify a product as a suggested gift for the gift campaign. In addition, the computing device may generate content for a network page for the social networking system, and the network page can include a user interface container in which the product is represented. The computing device may also obtain a selection of the product made via the network page and perform a transaction to order the product for the recipient.

Abstract: A performance analysis system analyzes the performance of affiliate sites that provide links to specific items in an electronic catalog, and identifies catalog items that can be listed by such affiliate sites to improve performance. An association mining component analyzes transaction data attributable to specific categories of affiliate sites to identify items that are frequently purchased in combination by users of such sites. The detected item associations are used to evaluate, for a given affiliate site, whether significant disparities exist between the expected and actual sales quantities of specific items. The results of the analysis are incorporated into affiliate-specific performance reports, which may include specific recommendations for improving performance. The disclosed methods may also be used to analyze the performance of, and provide recommendations to, online sellers within a marketplace or auction system.

Abstract: Technologies are described herein for extracting quotes from customer reviews regarding collections of items. An identifier of an individual item is received and customer reviews regarding a collection of items containing the individual item are retrieved. A collection of sentences is parsed from the retrieved customer reviews, and sentences that discuss the individual item are identified. A quote is extracted from the identified sentences and displayed to a customer in conjunction with information regarding the individual item. Similarly, individual items belonging to a collection of items are identified, and customer reviews regarding the individual items are retrieved. A collection of sentences are parsed from the retrieved customer reviews, and those sentences that discuss topics relevant to the collection of items are identified. A number of quotes are extracted from the identified sentences and displayed to a customer in conjunction with information regarding the collection of items.

Abstract: A computer-implemented service recommends digital works (and/or creators of works) to a user based on works currently or previously played or downloaded by the user on a player device or based on playlists stored on the player device. The works may be, for example, music files, video files, electronic books, or other digital content for playing by users. A user may thus obtain personalized recommendations that are based on works obtained from sources (web sites, physical CDs, etc.) that are independent of the recommendations system. In one embodiment, the service identifies pairs of works (and/or work creators) that are similar to each other by virtue of the relatively high frequency with which they co-occur on playlists or within play histories of users. The resulting mappings are used to provide recommendations to users.

Abstract: This disclosure describes systems and associated methods that can selectively filter seed behavior, e.g., user activity used to generate item recommendations. In some embodiments, seed behaviors and catalog items are associated with categories in an electronic catalog, and a particular seed behavior is used to generate user recommendations if it is more recent than a user's last purchase in the category of the seed behavior. For example, a user's activity in the TV category, e.g., viewing various TV models, may not be used to generate recommendations if the activity occurred prior to the user's purchase of a TV. As a result, in certain embodiments, additional TVs may not appear in the user's recommendations following her purchase, reflecting that the user has fulfilled her desire to purchase a TV.

Abstract: Data mining systems and methods are disclosed for generating data that is helpful to users in selecting between items represented in an electronic data repository, such as an electronic catalog. One disclosed data mining method generates pairwise comparison data for particular pairs of items based on aggregated user activity data reflective of item consideration actions and item selection actions of users. Another data mining method uses item viewing histories and item purchase histories of users in combination to assess whether particular items are good candidates for being purchased or recommended in combination. Statistical data resulting from one or both of these data mining methods may be presented to users (e.g., on electronic catalog pages) to assist users in making informed item selection decisions.

Abstract: Systems and methods are provided for generating social networking recommendations. A color preference of a first user may be determined from a color palette of a first image associated with the user and/or a color palette of an item associated with the user. Other users may be identified that have a similar color preference as the first user based at least in part on the determined color preference of the first user. Interactions between the first user and one or more other users having similar color preferences with respect to the first user may be facilitated. A social networking recommendation may be generated with respect to the one or more other users having similar color preferences with respect to the first user.