FIELD

The present disclosure relates generally to user recommendations in an e-commerce environment, and more particularly to a self-healing recommendation engine.

BACKGROUND

Recommendations provided to users of e-commerce are typically based on user characterization, such as on a user's demographic, previous purchases, and the like. However, there is a need in the art for computer-based methods and systems that improve future user recommendations.

SUMMARY

In an aspect, a computer-implemented method may include receiving, by a recommendation engine, a first user behavior data, the first user behavior data associated with a user action taken by a user, wherein the recommendation engine utilizes a recommendation model to generate recommendations to users based on user behavior data; labeling the user behavior; classifying the labeled user behavior; generating, by the recommendation engine, a recommendation to the user based on the classifying of the labeled user behavior; providing, by the recommendation engine, the recommendation to the user; receiving, by the recommendation engine, a second user behavior data, the second user behavior data associated with a user response by the user to the recommendation; and evaluating, by the recommendation engine, the second user behavior data and performing a corrective action to the recommendation model. In embodiments, assigning a confidence score may be in association with labeling the user behavior, wherein the evaluating is based on the received second user behavior and where the corrective action is generating an updated confidence score for the user behavior. The evaluating may be an evaluation of a process step in the labeling of the user behavior. The labeling may be performed by a labeling agent, wherein the corrective action further comprises at least one of adjusting or replacing the labeling agent. The evaluating may be an evaluation of a process step in the classifying of the labeled user behavior. The classifying may be performed by an agent, wherein the corrective action further comprises at least one of adjusting or replacing the agent. The evaluating may be an evaluation of a process step in the generation of the recommendation. The evaluating may be a combined evaluation of at least two of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation. The evaluating may be an evaluation of a process step in at least one of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation based on a data field of the second user behavior data. The corrective action may be a change to a weighting associated with the recommendation model. Weighting may be associated with the labeling of the user behavior, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the labeling. Weighting may be associated with the classifying of the labeled user behavior, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the classifying. Weighting may be associated with the generating of the recommendation, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the recommendation. The user action may be at least one of a browsing action, a communication action, a purchase action, or an installation action. The user action may be an action taken by the user in response to a recommendation.

In an aspect, a system may include a first computing device comprising a recommendation engine, the recommendation engine receiving a first user behavior data from a data facility, the first user behavior data associated with a user action taken by a user, wherein the recommendation engine utilizes a recommendation model to generate recommendations to users based on user behavior data, the first computing device configured to store a set of instructions that, when executed, cause the first computing device to: label the user behavior; classify the labeled user behavior; generate a recommendation directed to the user based on the classifying of the labeled user behavior; provide the recommendation to a second computing device; receive a second user behavior data, the second user behavior data associated with a user response by the user to the recommendation; and evaluate the second user behavior data and performing a corrective action to the recommendation model. In embodiments, assigning a confidence score may be in association with labeling the user behavior, wherein the evaluating is based on the received second user behavior and where the corrective action is generating an updated confidence score for the user behavior. The evaluating may be an evaluation of a process step in the labeling of the user behavior. The labeling may be performed by a labeling agent, wherein the corrective action further comprises at least one of adjusting or replacing the labeling agent. The evaluating may be an evaluation of a process step in the classifying of the labeled user behavior. The classifying may be performed by an agent, wherein the corrective action further comprises at least one of adjusting or replacing the agent. The evaluating may be an evaluation of a process step in the generation of the recommendation. The evaluating may be a combined evaluation of at least two of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation. The evaluating may be an evaluation of a process step in at least one of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation based on a data field of the second user behavior data. The corrective action may be a change to a weighting associated with the recommendation model. Weighting may be associated with the labeling of the user behavior, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the labeling. Weighting may be associated with the classifying of the labeled user behavior, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the classifying. Weighting may be associated with the generating of the recommendation, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the recommendation. The user action may be at least one of a browsing action, a communication action, a purchase action, or an installation action. The user action may be an action taken by the user in response to a recommendation.

In an aspect, a computer-implemented method for a user interface may include receiving, by a recommendation engine, a first user behavior data, the first user behavior data associated with a user action taken by a user, wherein the recommendation engine utilizes a recommendation model to generate recommendations to users based on user behavior data; labeling the user behavior; classifying the labeled user behavior; generating, by the recommendation engine, a recommendation to the user based on the classifying of the labeled user behavior; providing, by the recommendation engine, the recommendation to the user; receiving, by the recommendation engine, a second user behavior data, the second user behavior data associated with a user response by the user to the recommendation; and displaying an evaluation user interface for evaluating, by the recommendation engine, the second user behavior data and performing a corrective action to the recommendation model. In embodiments, the evaluating may be an evaluation of a process step in the labeling of the user behavior. The labeling may be performed by a labeling agent, wherein the corrective action further comprises at least one of adjusting or replacing the labeling agent. The evaluating may be an evaluation of a process step in the classifying of the labeled user behavior. The classifying may be performed by an agent, wherein the corrective action further comprises at least one of adjusting or replacing the agent. The evaluating may be an evaluation of a process step in the generation of the recommendation. The evaluating may be a combined evaluation of at least two of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation. The evaluating may be an evaluation of a process step in at least one of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation based on a data field of the second user behavior data. The corrective action may be a change to a weighting associated with the recommendation model. Weighting may be associated with the labeling of the user behavior, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the labeling. Weighting may be associated with the classifying of the labeled user behavior, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the classifying. Weighting may be associated with the generating of the recommendation, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the recommendation. The user action may be at least one of a browsing action, a communication action, a purchase action, or an installation action. The user action may be an action taken by the user in response to a recommendation.

In an aspect, a system may include a first computing device comprising a recommendation engine, the recommendation engine receiving a first user behavior data from a data facility, the first user behavior data associated with a user action taken by a user, wherein the recommendation engine utilizes a recommendation model to generate recommendations to users based on user behavior data, the first computing device configured to store a set of instructions that, when executed, cause the first computing device to: label the user behavior; classify the labeled user behavior; generate, by the recommendation engine, a recommendation to the user based on the classifying of the labeled user behavior; provide, by the recommendation engine, the recommendation to the user; receive, by the recommendation engine, a second user behavior data, the second user behavior data associated with a user response by the user to the recommendation; and display, on a second computing device, an evaluation user interface for evaluating, by the recommendation engine, the second user behavior data and performing a corrective action to the recommendation model. In embodiments, the evaluating may be an evaluation of a process step in the labeling of the user behavior. The labeling may be performed by a labeling agent, wherein the corrective action further comprises at least one of adjusting or replacing the labeling agent. The evaluating may be an evaluation of a process step in the classifying of the labeled user behavior. The classifying may be performed by an agent, wherein the corrective action further comprises at least one of adjusting or replacing the agent. The evaluating may be an evaluation of a process step in the generation of the recommendation. The evaluating may be a combined evaluation of at least two of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation. The evaluating may be an evaluation of a process step in at least one of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation based on a data field of the second user behavior data. The corrective action may be a change to a weighting associated with the recommendation model. Weighting may be associated with the labeling of the user behavior, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the labeling. Weighting may be associated with the classifying of the labeled user behavior, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the classifying. Weighting may be associated with the generating of the recommendation, wherein the weighting is associated with a weighting function that represents a confidence level on the goodness of the recommendation. The user action may be at least one of a browsing action, a communication action, a purchase action, or an installation action. The user action may be an action taken by the user in response to a recommendation.

In an aspect, a computer-implemented method for a user interface may include receiving, by a recommendation engine, a first user behavior data, the first user behavior data associated with a user action taken by a user, wherein the recommendation engine utilizes a recommendation model to generate recommendations to users based on user behavior data; labeling the user behavior; classifying the labeled user behavior; generating, by the recommendation engine, a recommendation to the user based on the classifying of the labeled user behavior; providing, by the recommendation engine, the recommendation to the user; receiving, by the recommendation engine, a second user behavior data, the second user behavior data associated with a negative user response by the user to the recommendation; displaying on a computer device the labeled user behavior and the negative user response; and displaying a root cause analysis, wherein the root cause analysis initiates a root cause processing function to evaluate the labeled user behavior based on the negative user response. In embodiments, the labeled user behavior may be further evaluated based on the labeling of the user behavior. The labeling may be performed by a labeling agent, wherein the root cause analysis recommends a corrective action further comprising at least one of adjusting or replacing the labeling agent. The labeled user behavior may be further evaluated based on the classifying of the labeled user behavior. The classifying may be performed by an agent, wherein the root cause analysis recommends a corrective action further comprising at least one of adjusting or replacing the agent. The labeled user behavior may be evaluated based on the generating of the recommendation. The evaluating may be a combined evaluation of at least two of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation. The evaluating may be an evaluation of a process step in at least one of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation based on a data field of the second user behavior data. The labeled user behavior may be evaluated based on a confidence score assigned to the labeled user behavior. The confidence score may be generated by a software-based agent. The confidence score may be generated by a human agent.

In an aspect, a system may include a first computing device comprising a recommendation engine, the recommendation engine receiving a first user behavior data from a data facility, the first user behavior data associated with a user action taken by a user, wherein the recommendation engine utilizes a recommendation model to generate recommendations to users based on user behavior data, the first computing device configured to store a set of instructions that, when executed, cause the first computing device to: label the user behavior; classify the labeled user behavior; generate, by the recommendation engine, a recommendation to the user based on the classifying of the labeled user behavior; provide, by the recommendation engine, the recommendation to the user; receive, by the recommendation engine, a second user behavior data, the second user behavior data associated with a negative user response by the user to the recommendation; display, on a second computer device, the labeled user behavior and the negative user response; and display, on the second computer device, a root cause analysis, wherein the root cause analysis initiates a root cause processing function to evaluate the labeled user behavior based on the negative user response. In embodiments, the labeled user behavior may be further evaluated based on the labeling of the user behavior. The labeling may be performed by a labeling agent, wherein the root cause analysis recommends a corrective action further comprising at least one of adjusting or replacing the labeling agent. The labeled user behavior may be further evaluated based on the classifying of the labeled user behavior. The classifying may be performed by an agent, wherein the root cause analysis recommends a corrective action further comprising at least one of adjusting or replacing the agent. The labeled user behavior may be evaluated based on the generating of the recommendation. The evaluating may be a combined evaluation of at least two of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation. The evaluating may be an evaluation of a process step in at least one of labeling the user behavior, classifying the labeled user behavior, and generating a recommendation based on a data field of the second user behavior data. The labeled user behavior may be evaluated based on a confidence score assigned to the labeled user behavior. The confidence score may be generated by a software-based agent. The confidence score may be generated by a human agent.

