Abstract: Techniques are described related to selecting content items, such as by enabling user analysis and control of product-related content items selected for display to users. The content items may include advertisements or other promotional materials, and the selecting of the content items may be performed as part of determining particular promotional materials to display or otherwise present to particular users in particular situations. User analysis and control of selected content items that are displayed on a target electronic site may be enabled by providing, as part of the target electronic site, additional selection-related functionality whose availability is restricted to one or more authorized users—for example, such additional restricted access information and user-selectable controls may be provided on a version of a Web page of an online retailer to enable the retailer to analyze and influence future content items selected for display on the online retailer's Web page(s).

Abstract: Techniques are described for determining personalized category-based product information for a user. The described techniques may in some situations be used by or on behalf of an online retailer to determine a sorted order of at least some products within a product category for the online retailer to display to a customer user on a category-specific Web page of the online retailer, such as based on a browse request by the customer for that category or on another identification of the category by the customer. The determination of particular personalized category-based sorted products for such a customer may in some situations be performed based at least in part on prior activities of the customer, including prior interactions by the customer with the online retailer (e.g., actions by the customer to view and/or purchase particular products, product brands, product sub-categories, product sizes, etc.).

Abstract: Techniques are described for selecting content items in various manners, such as by selecting product-related content items for display to consumer users. The content items may include advertisements or other promotional materials, and the selecting may be performed as part of determining particular promotional materials to display to particular users in particular situations, such as to accompany search results. In addition, the selecting of particular content items may be based on categorization of products indicated in search results and/or based on search terms used in searches performed on retail web sites. Particular content items are then selected based on the categorization, which may be for products distinct from those indicated in the search results, and may then be displayed with or otherwise in association with the search results.

Abstract: An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface.

Abstract: A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.

Abstract: Techniques are described related to automatically selecting and providing contextually appropriate information, such as in some situations by providing restricted commerce-related information and associated functionality to authorized users via corresponding Web pages of an online retailer or other online commerce site. The selecting and providing of contextually appropriate information is performed in at least some situations in an automated manner by a computer-implemented contextual information selection service of a first entity on behalf of one or more target online retailer sites or other target online commerce sites of one or more other second entities, with the selection service further determining particular promotional materials to display or otherwise present to particular customers of those target online commerce sites in at least some embodiments.

Abstract: A method of improving user set recommendations for product advertising including receiving a request for user set recommendations from any of a set of retailers where such request is related to one or more products, receiving from a plurality of user sets from one or more automated user recommendation systems, wherein the plurality of user sets are generated using different user models and using ensemble learning to select one or more most relevant user sets from the plurality of user sets.

Abstract: A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.

Abstract: A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.

Abstract: An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface.

Abstract: Techniques are described for dynamically generating recommendations for users, such as for products and other items. In at least some situations, the techniques include using multiple recommendation strategies, such as by aggregating recommendation results from multiple different recommendation strategies. Such recommendation strategies may have various forms, and may be based at least in part on data regarding prior interactions of numerous users with numerous items. In addition, information about current selections of a particular user may be gathered based at least in part on providing a GUI (“graphical user interface”) for display to the user that includes selectable information about numerous recommended items, and dynamically updating the displayed GUI with newly generated recommendations of items as the user makes selections of particular displayed recommended items (e.g.

Abstract: A demand driven process of collecting structured product and service information including the steps of: receiving a request for information from a requestor about a specific product or service, or attribute or class thereof, associating one or more potential responders with the request for information, providing the request to the potential responders, receiving and organizing one or more responses, and providing the organized responses to the requestor, all electronically. An arrangement for the same includes user computers for receiving requests for information, network communication devices for transmitting the requests to potential responders, memory for storing electronic responses and a controller for organizing, updating, and providing access to the electronic responses.

Abstract: Techniques are described for dynamically generating recommendations for users, such as for products and other items. In at least some situations, the techniques include using multiple recommendation strategies, such as by aggregating recommendation results from multiple different recommendation strategies. Such recommendation strategies may have various forms, and may be based at least in part on data regarding prior interactions of numerous users with numerous items. In addition, information about current selections of a particular user may be gathered based at least in part on providing a GUI (“graphical user interface”) for display to the user that includes selectable information about numerous recommended items, and dynamically updating the displayed GUI with newly generated recommendations of items as the user makes selections of particular displayed recommended items (e.g.

Abstract: An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface.

Abstract: An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface.

Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes receiving recommendation boost instructions, receiving a request for one or more recommendations, receiving a set of recommendations from one or more automated recommendation systems, with each recommendation system utilizing selection models or user models and modifying the set of product recommendations according to the recommendation boost instructions.

Abstract: A demand driven process of collecting and collaborating around reviews including the steps of: receiving a request for a review from a requestor, associating one or more potential responders with the request for a review, providing the request for a review to one or more potential responders, receiving and organizing one or more reviews from the responders, providing access to the organized reviews to the requestor and to the responders, and updating the accessible organized reviews as additional reviews are received, all electronically. An arrangement for the same includes user computers for receiving requests for a review, network communication devices for transmitting the requests to potential responders, memory for storing electronic reviews and a controller for organizing, updating, and providing access to the electronic reviews.

Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes presenting a user with a visual electronic interface adapted to receive recommendation boost instructions regarding a boost subject, receiving recommendation boost instructions via the visual electronic interface, wherein the recommendation boost instructions indicate how strongly the boost subject should be recommended or suppressed from being recommended, receiving a set of recommendations from one or more automated product recommendation systems, wherein each recommendation system utilizes one or more selection models or user models and modifying the set of recommendations according to the recommendation boost instructions.