Methods and systems for trend aware self-correcting entity relationship extraction are disclosed. For example, a method can include receiving a selected entity, determining a plurality of entities related to the selected entity, determining a plurality of most probable entities, calculating relevance scores, and displaying a subset of the plurality of most probable entities. The selected entity can be received on a network-based transaction system. The plurality of entities related to the selected entity can be determined based on a relationship score. The relationship score can represent navigation transitions, aggregated over time, between the selected entity and each of the plurality of entities. The plurality of most probable entities can be determined based on probabilities. Relevance scores can be calculated for each of the plurality of most probable entities. Finally, the subset of the plurality of most probable entities to be displayed can be determined according to the relevance scores.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system including one or more processors implementing processing modules comprising: an activity tracking module, within a network-based transaction system, to receive and process a selected entity, the selected entity published on the network-based transaction system; a relationship module, within the network-based transaction system, to determine a plurality of related entities based at least in part on aggregated navigation transitions between the selected entity and each related entity of the plurality of related entities; and a recommendation engine, within the network-based transaction system, to generate a recommendation set of related entities, for publication via the network-based transaction system over a network connection, from the plurality of related entities based at least in part on a velocity for each related entity of the plurality of related entities, wherein the velocity comprises a measure of directional change in popularity. 2. The system of claim 1, wherein the relationship module is further to select, from the plurality of related entities, a second plurality of related entities based at least in part on probabilities calculated within the relationship module based on the aggregated navigation transitions. 3. The system of claim 1, wherein the relationship module is further to weight more recent navigation transitions more heavily than older navigation transitions when aggregating the navigation transitions between entities. 4. The system of claim 2, wherein the recommendation module is to generate the recommendation set by calculating a relevance score for each related entity in the second plurality of related entities, wherein the relevance score is a function of probability and velocity. 5. The system of claim 1, wherein the relationship module is configured to calculate the velocity of the entity at least in part using a change in sales volume associated with the entity between a current time period and a past time period. 6. The system of claim 1, wherein the relationship module is configured to calculate the velocity of the entity at least in part using a change in page views associated with the entity between a current time period and a past time period. 7. A non-transitory computer-readable storage medium containing instructions that, when executed on a network-based transaction system, cause the network-based transaction system to perform operations comprising: receiving a selected entity on the network-based transaction system; determining a plurality of related entities based at least in part on aggregated navigation transitions between the selected entity and each related entity of the plurality of related entities; and generating a recommendation set of related entities, for publication via the network-based transaction system over a network connection, from the plurality of related entities based at least in part on a velocity for each related entity of the plurality of related entities, wherein the velocity is a measure of directional change in popularity. 8. The non-transitory computer-readable storage medium of claim 7, wherein the instructions that cause the network-based transaction system to generate the recommendation set include instructions that cause the network-based transaction system to calculate a relevance score for each related entity in the plurality of related entities, wherein the relevance score is a function of probability and velocity. 9. The non-transitory computer-readable storage medium of claim 8, wherein the instructions that cause the network-based transaction system to calculate the relevance score include instructions that cause the network-based transaction system to determine a category associated with the selected entity and filter the plurality of related entities in accordance with the category. 10. The non-transitory computer-readable storage medium of claim 7, wherein the instructions that cause the network-based transaction system to determine the plurality of related entities include instructions that cause the network-based transaction system to calculate and aggregate navigation transitions between the selected entity and the plurality of entities based on community behavior tracked by the network-based transaction system. 11. The non-transitory computer-readable storage medium of claim 7, wherein the instructions that cause the network-based transaction system to determine the plurality of related entities include instructions that cause the network-based transaction system to update the aggregated navigation transitions with current navigation transition data. 12. The non-transitory computer-readable storage medium of claim 11, wherein the instructions that cause the network-based transaction system to update the aggregated navigation transitions with current navigation transition data include instructions that cause the network-based transaction system to weight the current navigation transition data more heavily when calculating the aggregated navigation transitions. 13. A method comprising: receiving a selected entity on a network-based transaction system; determining, using one or more processors within the network-based transaction system, a plurality of related entities based at least in part on aggregated navigation transitions between the selected entity and each related entity of the plurality of related entities; and generating a recommendation set of related entities, for publication via the network-based transaction system over a network connection, from the plurality of related entities based at least in part on a velocity for each related entity of the plurality of related entities, wherein the velocity is a measure of directional change in popularity. 14. The method of claim 13, further comprising selecting, from the plurality of related entities, a second plurality of related entities based at least in part on probabilities calculated based on the aggregated navigation transitions. 15. The method of claim 14, wherein generating the recommendation set includes calculating a relevance score for each related entity in the second plurality of related entities, wherein the relevance score is a function of probability and velocity. 16. The method of claim 15, wherein calculating the relevance score includes determining a category associated with the selected entity and filtering the plurality of related entities in accordance with the category. 17. The method of claim 13, wherein determining the plurality of related entities includes updating the aggregated navigation transitions with current navigation transition data. 18. The method of claim 17, wherein the updating the aggregated navigation transitions weights the current navigation transition data more heavily when calculating the aggregated navigation transitions. 19. The method of claim 13, wherein determining the current navigation transition data includes tracking user session boundaries for determining related navigation history. 20. The method of claim 19, wherein tracking user session boundaries includes imposing artificial session boundaries based on at least one of idle time or browsing behavior.