Abstract: An improved user interface and method are provided for presenting recommendations to a user when the user adds an item to a shopping cart. In response to the shopping cart add event, a page generation process generates and returns a page that includes a recommendations portion and a condensed view of the shopping cart. The recommendations portion preferably includes multiple recommendation sections, each of which displays a different respective set of recommended items selected according to a different respective recommendation or selection algorithm (e.g., recommendations based on shopping cart contents, recommendations based on purchase history, etc.). The condensed shopping cart view preferably lacks controls for editing the shopping cart, and lacks certain types of product information, making more screen real estate available for the display of the recommendations content. A link to a full shopping cart page allows the user to edit the shopping cart and view expanded product descriptions.

Abstract: A combine having a camera system is disclosed in which pictures of the ground in front of the vehicle are processed to extract the slope and intercept of a characteristic line indicative of the boundary between the cut and uncut portions of the field. A fuzzy logic algorithm compares the slope and intercept using four input membership functions and a single output membership function. If the slope and intercept extracted from the image are deemed acceptable, they are used to navigate the vehicle through the field. If they are not acceptable, the combine continues to use slope and intercept values previously deemed acceptable to guide the vehicle. This process of image data extraction and quality analysis continues repetitively as the combine traverses the field harvesting crops.

Abstract: A computer-implemented service recommends products or other items to a user based on a set of items known to be of interest to the user, such as a set of items currently in the user's electronic shopping cart. In one embodiment, the service identifies items that are currently in the user's shopping cart, and uses these items to generate a list of additional items that are predicted to be of interest to the user, wherein an additional item is selected to include in the list based in-part upon whether that item is related to more than one of the items in the user's shopping cart. The item relationships are preferably determined by an off-line process that analyzes user purchase histories to identify correlations between item purchases. The additional items are preferably displayed to the user when the user views the contents of the shopping cart.

Abstract: A computer-implemented service recommends items to a user based on items previously selected by the user, such as items previously purchased, viewed, or placed in an electronic shopping cart by the user. The items may, for example, be products represented within a database of an online merchant. In one embodiment, the service generates the recommendations using a previously generated table that maps items to respective lists of “similar” items. To generate the table, historical data indicative of users' affinities for particular items is processed periodically to identify correlations between item interests of users (e.g., items A and B are similar because a large portion of those who selected A also selected B). Personal recommendations are generated by accessing the table to identify items similar to those selected by the user. In one embodiment, items are recommended based on the current contents of a user's shopping cart.

Abstract: A recommendations service recommends items to individual users based on a set of items that are known to be of interest to the user, such as a set of items previously purchased by the user. In the disclosed embodiments, the service is used to recommend products to users of a merchant's Web site. The service generates the recommendations using a previously-generated table which maps items to lists of “similar” items. The similarities reflected by the table are based on the collective interests of the community of users. For example, in one embodiment, the similarities are based on correlations between the purchases of items by users (e.g., items A and B are similar because a relatively large portion of the users that purchased item A also bought item B). The table also includes scores which indicate degrees of similarity between individual items.

Abstract: A recommendation service is disclosed which uses collaborative filtering techniques to recommend books to users of a Web site. The Web site includes a catalog of the various titles that can be purchased via the site. The recommendation service includes a database of titles that have previously been rated and that can therefore be recommended by the service using collaborative filtering methods. At least initially, the titles and title categories (genres) that are included within this database (and thus included within the service) are respective subsets of the titles and categories included within the catalog. As users browse the site to read about the various titles contained within the catalog, the users are presented with the option of rating specific titles, including titles that are not currently included within the service. The ratings information obtained from this process is used to automatically add new titles and categories to the service.