Behavioral prediction for targeted end users is described. In one or more example embodiments, a computer-readable storage medium has multiple instructions that cause one or more processors to perform multiple operations. Targeted selectstream data is obtained from one or more indications of data object requests corresponding to a targeted end user. A targeted directed graph is constructed based on the targeted selectstream data. A targeted graph feature vector is computed based on one or more invariant features associated with the targeted directed graph. A behavioral prediction is produced for the targeted end user by applying a prediction model to the targeted graph feature vector. In one or more example embodiments, the prediction model is generated based on multiple graph feature vectors respectively corresponding to multiple end users. In one or more example embodiments, a tailored opportunity is determined responsive to the behavioral prediction and issued to the targeted end user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A device implemented at least partially in hardware to predict targeted end user behavior to facilitate opportunity tailoring, the device comprising: a selectstream data obtainment module configured to obtain from a storage device selectstream data corresponding to multiple end users; a directed graph construction module configured to construct multiple directed graphs, each respective directed graph constructed based on the selectstream data corresponding to a respective end user of the multiple end users, each directed graph associated with one or more invariant features; a graph feature vector computation module configured to compute multiple graph feature vectors, each respective graph feature vector computed based on the one or more invariant features associated with a respective directed graph of the multiple directed graphs; and a prediction model generation module configured to generate a prediction model based on the multiple graph feature vectors using a machine learning system, the prediction model configured to produce a behavioral prediction responsive to targeted selectstream data that corresponds to a targeted end user. 2. The device as described in claim 1, wherein each directed graph of the multiple directed graphs is representative of a path of travel of a corresponding end user of the multiple end users as the corresponding end user traverses over multiple data objects. 3. The device as described in claim 1, wherein the directed graph construction module is further configured to: assign vertices of a given directed graph of the multiple directed graphs based on data objects traversed by a corresponding end user of the multiple end users; and define respective directed edges of the given directed graph based on respective pairs of source data objects and destination data objects that are traversed by the corresponding end user, the pairs of source data objects and destination data objects identified in the selectstream data of the corresponding end user. 4. The device as described in claim 1, wherein the one or more invariant features comprise at least one intuitive invariant feature that translates to a physical marketing concept and at least one abstract invariant feature that pertains to directed graph topology. 5. The device as described in claim 1, wherein the graph feature vector computation module is further configured to: compute multiple respective real values that correspond to multiple respective invariant features of the one or more invariant features for each directed graph of the multiple directed graphs. 6. The device as described in claim 1, wherein the prediction model generation module is further configured to: perform a clustering operation to separate the multiple graph feature vectors into multiple clusters based on one or more similarities between or among different ones of the multiple graph feature vectors. 7. The device as described in claim 1, wherein the prediction model generation module is further configured to: perform a training operation on a classifier using at least a portion of the multiple graph feature vectors as a training set of graph feature vectors with each graph feature vector of the at least a portion associated with a classification category. 8. The device as described in claim 1, further comprising: a prediction model application module configured to apply the prediction model to a targeted graph feature vector that is derived from the targeted selectstream data to produce a behavioral prediction for the targeted end user. 9. A system implemented to predict targeted end user behavior to facilitate opportunity tailoring, the system comprising: one or more computing devices implemented at least partially in hardware, the one or more computing devices configured to perform operations comprising: obtaining targeted selectstream data from one or more indications of data object requests corresponding to a targeted end user; constructing a targeted directed graph based on the targeted selectstream data; computing a targeted graph feature vector based on one or more invariant features associated with the targeted directed graph; and producing a behavioral prediction for the targeted end user by applying a prediction model to the targeted graph feature vector. 10. The system as described in claim 9, wherein the data object requests comprise requests for website pages that are each identified by a uniform resource locator (URL); and wherein the obtaining comprises: receiving, from an end-user device of the targeted end user, the data object requests for the website pages that are each identified by a URL. 11. The system as described in claim 9, wherein the behavioral prediction comprises at least one of: an indication that the targeted end user is likely to make a purchase, an indication that the targeted end user is likely to join a customer associative program, or an indication that the targeted end user is likely to abandon an electronic cart without completing a purchase. 12. The system as described in claim 9, wherein the producing comprises: identifying one or more graph feature vectors based on a similarity to the targeted graph feature vector, the one or more graph feature vectors corresponding to one or more end users that exhibited a particular behavioral attribute; and assigning to the behavioral prediction the particular behavioral attribute. 13. The system as described in claim 12, wherein a measure of the similarity comprises an L-p norm distance; and wherein the identifying comprises: determining the L-p norm distance between the targeted graph feature vector and the one or more graph feature vectors. 14. The system as described in claim 9, wherein the prediction model comprises a clustering-based prediction model; and wherein the producing comprises: determining, using the clustering-based prediction model, a relationship between the targeted end user and an individual segment of multiple segments, each segment of the multiple segments associated with end users that exhibited a particular behavioral attribute. 15. The system as described in claim 9, wherein the prediction model comprises a classifying-based prediction model; and wherein the producing comprises: assigning to the targeted end user a classification category of multiple classification categories, each classification category of the multiple classification categories associated with a particular behavioral attribute. 16. The system as described in claim 9, wherein the one or more computing devices are configured to perform operations further comprising: determining a tailored opportunity responsive to the behavioral prediction; and issuing the tailored opportunity to the targeted end user. 17. The system as described in claim 16, wherein the tailored opportunity comprises a discount on a purchase of a product; and wherein the issuing comprises: presenting the discount to the targeted end user. 18. A method implemented to produce a behavioral prediction for a targeted end user as guidance for providing a tailored opportunity in a digital environment in which end users traverse over data object collections via links between pairs of data objects, the method comprising: generating a prediction model based on multiple paths of travel that correspond to multiple end users and on one or more behavioral attributes associated with the multiple end users; receiving indications of data objects selected by a targeted end user via an end-user device; determining a targeted path of travel over the data objects based on the indications of the data objects selected by the targeted end user; computing a characterization of the targeted path of travel over the data objects, the characterization representative of dynamic traversals between pairs of the data objects; and applying the prediction model to the characterization of the targeted path of travel to produce a behavioral prediction for the targeted end user. 19. The method as described in claim 18, further comprising: issuing for the targeted end user a tailored opportunity based on the behavioral prediction. 20. The method as described in claim 18, wherein the applying comprises: producing the behavioral prediction based on a measure of similarity between the characterization of the targeted path of travel of the targeted end user and characterizations of at least a portion of the multiple paths of travel that correspond to the multiple end users.