EP-4738226-A1 - PREDICTIVE RECOMMENDATION GENERATION

Abstract

In implementations of systems and procedures for a service provider system, a computing device implements predictive recommendation generation. A registration component is employed to register a specific interaction sequence defining an order of user interactions to be performed by a single user during a session on a platform. The specific interaction sequence is predicted to proceed to a request for a recommendation result by the single user. Additionally, an interaction monitor is configured to monitor, in real-time, interaction sequences of a plurality of users. In response to detecting the specific interaction sequence for a user of the plurality of users, the interaction monitor triggers, in real-time, pre-calculation of the recommendation result for the user, and a cache stores the pre-calculated recommendation result.

Inventors

• NI, Kaichen
• QIN, Rou
• SHEN, Yizhou
• YU, He
• Zhou, Lu

Assignees

• eBay Inc.

Dates

Publication Date

20260506

Application Date

20251016

Claims (15)

1. A system, comprising: a registration component to register a specific interaction sequence defining an order of user interactions to be performed by a single user during a session on a platform, the specific interaction sequence predicted to proceed to a request for a recommendation for the single user; an interaction monitor to: monitor, in real-time, interaction sequences of a plurality of users; and in response to detecting the specific interaction sequence for a user of the plurality of users, performing, in real-time, pre-calculation of the recommendation for the single user; and a cache to store the pre-calculated recommendation.
2. The system of claim 1, further comprising a domain service to: retrieve the pre-calculated recommendation from the cache in response to the single user requesting the recommendation; and output the pre-calculated recommendation.
3. The system of claim 2, wherein the cache is configured to store the recommendation indexed to an entity level specified by the domain service.
4. The system of any one of the preceding claims, further comprising a machine-learning model to pre-calculate the recommendation; wherein the interaction monitor may select data for the machine-learning model to use for pre-calculating the recommendation based on the single user and the specific interaction sequence.
5. The system of any one of the preceding claims, wherein the platform supports a plurality of item listings, and a content of the recommendation is determined based on one or more of the item listings associated with the single user; wherein the content of the recommendation may include one or more promotions redeemable on the platform.
6. A method implemented by a computing device, comprising: receiving input specifying a registered interaction sequence, the registered interaction sequence defining interactions performable in a specified order during a user session on a platform; monitoring, in real-time, interaction sequences performed by users of the platform; responsive to determining that one of the interaction sequences performed by one of the users includes each of the interactions of the registered interaction sequence in the specified order, immediately generating a recommendation associated with the user and storing the recommendation to a cache.
7. The method of claim 6, further comprising: responsive to detecting that a trigger has occurred, retrieving the recommendation from the cache and outputting the recommendation to the user wherein storing the recommendation to the cache may include associating the recommendation in the cache with the user via an index in the cache.
8. The method of claim 6 or 7, wherein generating the recommendation associated with the user is performed via a machine-learning model; wherein the method may further comprise selecting data for the machine-learning model to use for generating the recommendation associated with the user based on data describing attributes of the user and the registered interaction sequence.
9. The method of any one of claims 6 to 8, further comprising: monitoring, in real-time, interactions of the user with the platform; detecting a trigger from the interactions; and responsive to detecting the trigger, retrieving the recommendation from the cache and outputting the recommendation to a user device via the platform.
10. The method of any one of claims 6 to 9, further comprising: monitoring, in real-time, interactions of the user with the platform; determining whether a trigger is present in the monitored interactions within a duration; and responsive to determining that the trigger is not present in the monitored interactions within the duration, discarding the recommendation from the cache.
11. One or more transitory or non-transitory computer-readable storage media storing instructions thereon which, when executed by one or more computing devices, cause the one or more computing devices to perform operations comprising: receiving input to register a specific interaction sequence defining an order of user interactions to be performed by a single user during a session on a platform; monitoring, in real-time, interaction sequences of a plurality of users; responsive to detecting the specific interaction sequence for a user of the plurality of users, performing, in real-time, pre-calculation of a recommendation for the user; storing the pre-calculated recommendation to a cache.
12. The one or more transitory or non-transitory computer-readable storage media of claim 11, wherein the instructions further comprise: retrieving the pre-calculated recommendation from the cache in response to the single user requesting the recommendation; and outputting the pre-calculated recommendation.
13. The one or more transitory or non-transitory computer-readable storage media of claim 11 or 12, wherein the instructions further comprise associating the recommendation in the cache with the single user via an index of the cache.
14. The one or more transitory or non-transitory computer-readable storage media of any one of claims 11 to 13, wherein the instructions further comprise performing the pre-calculation of the recommendation for the user using a machine-learning model; wherein the instructions may further comprise selecting data for the machine-learning model to use for pre-calculating the recommendation based on the single user and the specific interaction sequence.
15. The one or more transitory or non-transitory computer-readable storage media of any one of claims 11 to 14, wherein the instructions further comprise determining a content of the recommendation based on the platform.

Description

BACKGROUND Computing devices implementing platforms that support item listings, such as electronic commerce platforms, can experience different amounts of computational load during different conditions. Some interactions between users and platforms can lead to higher computational loads than other interactions. Interactions associated with higher computational loads can result in consumption of shared resources, such as memory and processing power, that are also used for other tasks. During conditions in which high computational load interactions occur, the consumption of shared resources can lead to increased latency associated with performing other tasks. Additionally, interactions causing high computational loads can take more time to complete than interactions associated with lower computational loads. SUMMARY Techniques for predictive recommendation generation are described. In one or more implementations, a system includes a registration component to register a specific interaction sequence defining an order of user interactions to be performed by a single user during a session on a platform. The specific interaction sequence is predicted to proceed to a request for a recommendation result by the single user. Additionally, an interaction monitor is configured to monitor, in real-time, interaction sequences of a plurality of users. In response to detecting the specific interaction sequence for a user of the plurality of users, the interaction monitor triggers, in real-time, pre-calculation of the recommendation result for the user, and a cache stores the pre-calculated recommendation result. This Summary introduces a selection of concepts in a simplified form that are further described below in the Detailed Description. As such, this Summary is not intended to identify essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS The detailed description is described with reference to the accompanying figures. Entities represented in the figures are indicative of one or more entities and thus reference is made interchangeably to single or plural forms of the entities in the discussion. FIG. 1 is an illustration of an environment in an example implementation that is operable to employ predictive recommendation generation as described herein.FIG. 2 depicts an example implementation showing operation of a registration module and interaction monitor of a service provider system.FIG. 3 depicts an example implementation showing operation of a pre-calculation module configured to generate a recommendation and store the recommendation to a cache.FIG. 4 depicts an example implementation showing operation of the interaction monitor to detect a trigger and provide the recommendation responsive to the trigger.FIG. 5 is an illustration of another environment in an example implementation that is operable to employ predictive recommendation generation as described herein.FIG. 6 is an illustration depicting data sources for predictive recommendation generation.FIG. 7 is an illustration of a graphical user interface employed for registration of an interaction sequence for predictive recommendation generation.FIG. 8 shows a flow diagram depicting a procedure in an example implementation which includes predictive recommendation generation.FIG. 9 shows a flow diagram depicting a procedure in an example implementation which includes a recommendation pre-calculated via predictive recommendation generation.FIG. 10 illustrates an example system that includes an example computing device that is representative of one or more computing systems and/or devices for implementing the various techniques described herein. DETAILED DESCRIPTION Overview Service provider systems implement platforms supporting item listings, such as electronic commerce platforms. In some situations, such platforms are configured to provide recommendations to users. Recommendations may include, for example, advertisements for products and/or services, suggestions for increasing views and visibility of item listings on the platform, or indications of item listings similar to those associated with a particular viewer. In order to provide recommendations, service provider systems implementing such platforms may perform various calculations associated with generating the recommendations. Such calculations may be computationally intensive and may involve prolonged calculation times and/or consumption of resources of a service provider system that are also used for performing other operations. For example, resources such as memory and processor load may be used by the service provider system to perform calculations for generating recommendations as well as to perform operations to maintain a stability and network connectivity of the service provider system. In some conventional approaches, computationally intensive calculations, such as calculatio