Abstract: When an end user opens an e-mail the user's e-mail client requests the image content based on the image tag from a recommendation platform and sends an identification to the recommendation platform as part of the image URL. The user ID or seed item is looked up in the recommendation platform database and associated behavioral data and any applicable rules to generate content for the e-mail and the content is then displayed in the e-mail as an image. When the image is engaged, a request is sent to the recommendation platform that references the user or request identifier and a logical location in the image where the click occurred, the image location is looked up along with the user or request identifier to present the correct page or content for the user.

Abstract: Systems and methods are described for performing the dynamic generation of correlation scores between arbitrary objects. When a behavioral event is recorded, that is to say when an end user interacts with multiple objects, relationships between objects are created. These relationships are maintained as a list. When a request for correlated items is requested based upon a seed object, a list of correlated items is dynamically created through the generation of a pivot set and a scoring algorithm to compute the list of correlated items.

Abstract: A relationship server tracks end-user interactions across multiple web sites and generates recommendations. The web sites observe relationships established by end-user interactions. If end-users provide the same personally identifiable information to multiple web sites, the sites generate the same unique identifier for those end-users. The web sites send messages to the relationship server that reference the end-users using the identifiers and describe the relationships observed for the end-users. The relationship server receives messages from multiple web sites and canonicalizes them to produce an efficient representation of the relationships. Upon receiving a message requesting a recommendation based on an item, the relationship server performs collaborative filtering using the relationship data to identify a list of items to recommend. The relationship server sends the recommendations to the requesting entity and the recommendations are presented to the end-user.