Dynamic model-based access right predictions
Systems and methods may use models to generate predictions of specific access rights for users. Further, systems and methods may generate the predictions in an environment in which the availability of the specific access rights change frequently. The access rights, predicted using embodiments described herein, may be both available and associated with user affinities. An interface associated with the primary load management system may be configured to display the predicted access rights for a user operating a user device.
1. A method for recommending resources to a user device, the method comprising:
storing a set of access rights including one or more subsets of access rights, each subset of the one or more subsets of access rights being associated with the resource, and each access right of each subset of the one or more subsets of access rights enabling access to the associated resource during a defined time period and being assignable to the user device;
receiving a communication from a user device, the communication corresponding to a request for assignment of an access right to a resource;
querying a data store for a real-time state of availability of access rights, the real-time state of availability of access rights representing a set of unassigned access rights;
accessing one or more user data sources associated with the user device, each of the one or more user data sources including a user attribute that characterizes the user or the user device;
generating a first structured data set including user data retrieved from one or more user data sources associated with the user device;
accessing one or more performer data sources associated with one or more performers, each of the one or more performer data sources including a performer attribute associated with a performer;
generating a second structured data set including performer data retrieved from the one or more performer data sources;
generating a model for predicting which unassigned access rights to present to the user device in response to receiving the communication, the generation of the model using each of the first structured data set and the second structured data set;
determining, using the generated model, an affinity parameter for each performer associated with the second structured data set, the affinity parameter representing an affinity to a particular performer;
generating, using the model, a prediction for performer based at least in part on the determined affinity parameter for the particular performer that corresponds to a portion of the performer data, the prediction including at least one unassigned access right for the particular performer that corresponds to the portion of the performer data associated with the second structured data set; and
displaying an event prediction, the displayed event prediction enabling the user device to request assignment of the at least one unassigned access right to the particular performer.
2. The method for recommending resources to a user device, as recited in claim 1 , wherein the first structured data set includes a vector, wherein the vector includes one or more items of user data, and wherein each item of user data is based on a user attribute associated with the user device.
3. The method for recommending resources to a user device, as recited in claim 1 , wherein the second structured data set includes a vector, wherein the vector includes one or more items of the performer data, and wherein each item of the performer data is based on the performer attribute.
4. The method for recommending resources to a user device, as recited in claim 1 , wherein the affinity parameter is determined by performing a dot product between the first structured data set and the second structured data set.
5. The method for recommending resources to a user device, as recited in claim 1 , wherein the second structured data set includes a vector with one or more elements, wherein each element of the vector includes a particular performer attribute associated with the particular performer.
6. The method for recommending resources to a user device, as recited in claim 1 , wherein the second structured data set includes a vector with one or more elements, wherein each element of the vector includes a previous indication of an affinity initiated by a user associated with the user device, and wherein the previous indication of the affinity is associated with the particular performer.
7. The method for recommending resources to a user device, as recited in claim 6 , wherein the previous indication of affinity initiated by the user includes at least one of a previous assignment to an access right to the particular performer and an indication of affinity retrieved from a social media network.
8. A system for recommending resources to a user device, the system comprising:
one or more processors; and
a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more processors, cause the one or more processors to perform operations including:
storing a set of access rights including one or more subsets of access rights, each subset of the one or more subsets of access rights being associated with the resource, and each access right of each subset of the one or more subsets of access rights enabling access to the associated resource during a defined time period and being assignable to the user device;
receiving a communication from a user device, the communication corresponding to a request for assignment of an access right to a resource;
querying a data store for a real-time state of availability of access rights, the real-time state of availability of access rights representing a set of unassigned access rights;
accessing one or more user data sources associated with the user device, each of the one or more user data sources including a user attribute that characterizes the user or the user device;
generating a first structured data set including user data retrieved from one or more user data sources associated with the user device;
accessing one or more performer data sources associated with one or more performers, each of the one or more performer data sources including a performer attribute associated with a performer;
generating a second structured data set including performer data retrieved from the one or more performer data sources;
generating a model for predicting which unassigned access rights to present to the user device in response to receiving the communication, the generation of the model using each of the first structured data set and the second structured data set;
determining, using the generated model, an affinity parameter for each performer associated with the second structured data set, the affinity parameter representing an affinity to a particular performer;
generating, using the model, a prediction for performer based at least in part on the determined affinity parameter for the particular performer that corresponds to the portion of the performer data, the prediction including at least one unassigned access right for the particular performer that corresponds to a portion of the performer data associated with the second structured data set; and
displaying an event prediction, the displayed event prediction enabling the user device to request assignment of the at least one unassigned access right to the particular performer.
9. The system for recommending resources to a user device, as recited in claim 8 , wherein the first structured data set includes a vector, wherein the vector includes one or more items of user data, and wherein each item of user data is based on a user attribute associated with the user device.
10. The system for recommending resources to a user device, as recited in claim 8 , wherein the second structured data set includes a vector, wherein the vector includes one or more items of the performer data, and wherein each item of the performer data is based on the performer attribute.
11. The system for recommending resources to a user device, as recited in claim 8 , wherein the affinity parameter is determined by performing a dot product between the first structured data set and the second structured data set.
12. The system for recommending resources to a user device, as recited in claim 8 , wherein the second structured data set includes a vector with one or more elements, wherein each element of the vector includes a particular performer attribute associated with the particular performer.
13. The system for recommending resources to a user device, as recited in claim 8 , wherein the second structured data set includes a vector with one or more elements, wherein each element of the vector includes a previous indication of an affinity initiated by a user associated with the user device, and wherein the previous indication of the affinity is associated with the particular performer.
14. The system for recommending resources to a user device, as recited in claim 13 , wherein the previous indication of affinity initiated by the user includes at least one of a previous assignment to an access right to the particular performer and an indication of affinity retrieved from a social media network.
15. A non-transitory machine-readable storage medium storing instructions which, when executed on a processor, cause the processor to perform operations including:
storing a set of access rights including one or more subsets of access rights, each subset of the one or more subsets of access rights being associated with the resource, and each access right of each subset of the one or more subsets of access rights enabling access to the associated resource during a defined time period and being assignable to the user device;
receiving a communication from a user device, the communication corresponding to a request for assignment of an access right to a resource;
querying a data store for a real-time state of availability of access rights, the real-time state of availability of access rights representing a set of unassigned access rights;
accessing one or more user data sources associated with the user device, each of the one or more user data sources including a user attribute that characterizes the user or the user device;
generating a first structured data set including user data retrieved from one or more user data sources associated with the user device;
accessing one or more performer data sources associated with one or more performers, each of the one or more performer data sources including a performer attribute associated with a performer;
generating a second structured data set including performer data retrieved from the one or more performer data sources;
generating a model for predicting which unassigned access rights to present to the user device in response to receiving the communication, the generation of the model using each of the first structured data set and the second structured data set;
determining, using the generated model, an affinity parameter for each performer associated with the second structured data set, the affinity parameter representing an affinity to a particular performer;
generating, using the model, a prediction for performer based at least in part on the determined affinity parameter for the particular performer that corresponds to a portion of the performer data, the prediction including at least one unassigned access right for the particular performer that corresponds to the portion of the performer data associated with the second structured data set; and
displaying an event prediction, the displayed event prediction enabling the user device to request assignment of the at least one unassigned access right to the particular performer.
16. The method for recommending resources to a user device, as recited in claim 15 , wherein the first structured data set includes a vector, wherein the vector includes one or more items of user data, and wherein each item of user data is based on a user attribute associated with the user device.
17. The method for recommending resources to a user device, as recited in claim 15 , wherein the second structured data set includes a vector, wherein the vector includes one or more items of the performer data, and wherein each item of the performer data is based on the performer attribute.
18. The method for recommending resources to a user device, as recited in claim 15 , wherein the affinity parameter is determined by performing a dot product between the first structured data set and the second structured data set.
19. The method for recommending resources to a user device, as recited in claim 15 , wherein the second structured data set includes a vector with one or more elements, wherein each element of the vector includes a particular performer attribute associated with the particular performer.
20. The method for recommending resources to a user device, as recited in claim 15 , wherein the second structured data set includes a vector with one or more elements, wherein each element of the vector includes a previous indication of an affinity initiated by a user associated with the user device, and wherein the previous indication of the affinity is associated with the particular performer.