Method and system for improving content searching in a question and answer customer support system by using a crowd-machine learning hybrid predictive model
First Claim
1. A computing system implemented method for using a hybrid predictive model to respond to search queries for customer support content in a question and answer customer support system, the method comprising:
- storing existing customer support content data in memory allocated for use by a question and answer customer support system, the existing customer support content data representing existing customer support content entries having groups of combinations of existing search queries and existing responses, the existing search queries having been submitted to the question and answer customer support system by prior users and the existing responses having been submitted to the question and answer customer support system in response to the existing search queries;
receiving search query data from a user, the search query data representing a current search query for customer support content from the question and answer customer support system;
training a hybrid predictive model through unsupervised learning using at least a combination of machine learning content and crowdsourced user feedback to identify the customer support content of the current search query, resulting in hybrid predictive model data, the training at least including applying one or more additional natural language processing algorithms to the crowdsourced user feedback to extract one or more features of the customer support content from the user feedback, the training including consideration of a length of reference material and a style of a reference material;
applying the search query data to the predictive model data to generate content selection score data representing two or more content selection scores, at least one of the two or more content selection scores representing a relevance of the existing customer support content entries to the search query data, and at least one of the two or more content selection scores representing a quality of the customer support content, the quality-related content selection score being provided by a content quality predictive model;
selecting one of the existing customer support content entries at least partially based on the one or more content selection scores;
generating user experience display data that includes at least one user experience page and that includes the one of the existing customer support entries, the user experience display data representing a user experience display that renders the one of the existing customer support content entries in the at least one user experience page;
providing the user experience display data to the user, in response to receiving the search query data from the user;
requesting user feedback data from the user, to enable the question and answer customer support system to improve performance of the hybrid predictive model, the user feedback data representing crowdsourced user feedback related to the one of the existing customer support content entries that is included in the user experience display data; and
updating, upon receipt of user feedback data from the user, the hybrid predictive model at least partially based on the user feedback data, the received feedback including data regarding a quality of writing, accuracy and content and search ranking.
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Abstract
A method and system improves content searching in a question and answer customer support system by using a crowd-machine learning hybrid predictive model, according to one embodiment. The question and answer customer support system determines which customer support content to provide to users by using the hybrid predictive model, according to one embodiment. The question and answer customer support system receives a search query from a user and applies the search query (or a representation of the search query) to the hybrid predictive model, according to one embodiment. The hybrid predictive model generates a likelihood that particular customer support content is relevant to a user'"'"'s search query, according to one embodiment. The question and answer customer support system acquires user feedback from users and updates/trains the hybrid predictive model based on the user feedback, according to one embodiment.
166 Citations
23 Claims
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1. A computing system implemented method for using a hybrid predictive model to respond to search queries for customer support content in a question and answer customer support system, the method comprising:
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storing existing customer support content data in memory allocated for use by a question and answer customer support system, the existing customer support content data representing existing customer support content entries having groups of combinations of existing search queries and existing responses, the existing search queries having been submitted to the question and answer customer support system by prior users and the existing responses having been submitted to the question and answer customer support system in response to the existing search queries; receiving search query data from a user, the search query data representing a current search query for customer support content from the question and answer customer support system; training a hybrid predictive model through unsupervised learning using at least a combination of machine learning content and crowdsourced user feedback to identify the customer support content of the current search query, resulting in hybrid predictive model data, the training at least including applying one or more additional natural language processing algorithms to the crowdsourced user feedback to extract one or more features of the customer support content from the user feedback, the training including consideration of a length of reference material and a style of a reference material; applying the search query data to the predictive model data to generate content selection score data representing two or more content selection scores, at least one of the two or more content selection scores representing a relevance of the existing customer support content entries to the search query data, and at least one of the two or more content selection scores representing a quality of the customer support content, the quality-related content selection score being provided by a content quality predictive model; selecting one of the existing customer support content entries at least partially based on the one or more content selection scores; generating user experience display data that includes at least one user experience page and that includes the one of the existing customer support entries, the user experience display data representing a user experience display that renders the one of the existing customer support content entries in the at least one user experience page; providing the user experience display data to the user, in response to receiving the search query data from the user; requesting user feedback data from the user, to enable the question and answer customer support system to improve performance of the hybrid predictive model, the user feedback data representing crowdsourced user feedback related to the one of the existing customer support content entries that is included in the user experience display data; and updating, upon receipt of user feedback data from the user, the hybrid predictive model at least partially based on the user feedback data, the received feedback including data regarding a quality of writing, accuracy and content and search ranking. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computing system implemented method for using a hybrid predictive model to respond to search queries for customer support content in a question and answer customer support system, the method comprising:
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storing existing customer support content data in memory allocated for use by a question and answer customer support system, the existing customer support content data representing existing customer support content entries, the existing customer support content entries including groups of combinations of existing search queries and existing responses, the existing search queries having been submitted to the question and answer customer support system by prior users and the existing responses having been submitted to the question and answer customer support system in response to the existing search queries; receiving search query data from a user, the search query data representing a current search query for customer support content from the question and answer customer support system; training a hybrid predictive model through unsupervised learning using at least a combination of machine learning content and crowdsourced user feedback to identify the customer support content of the current search query, resulting in hybrid predictive model data, the training at least including applying one or more additional natural language processing algorithms to the crowdsourced user feedback to extract one or more features of the customer support content from the user feedback, the training including consideration of a length of reference material and a style of a reference material; applying the search query data to the predictive model data to generate content selection score data representing two or more content selection scores that represent a likelihood of satisfaction of the user with the existing customer support entries, at least one of the two or more content selection scores representing a quality of the customer support content, the quality-related content selection score being provided by a content quality predictive model; selecting one of the existing customer support content entries at least partially based on the one or more content selection scores; generating user experience display data that includes at least one user experience page and that includes the one of the existing customer support entries, the user experience display data representing a user experience display that renders the one of the existing customer support content entries in the at least one user experience page; providing the user experience display data to the user, in response to receiving the search query data from the user; requesting user feedback data from the user, to enable the question and answer customer support system to improve performance of the hybrid predictive model, the user feedback data representing crowdsourced user feedback related to the one of the existing customer support content entries that is included in the user experience display data; and updating, upon receipt of user feedback data from the user, the hybrid predictive model at least partially based on the user feedback data, the received feedback including data regarding a quality of writing, perceived accuracy or content and search ranking. - View Dependent Claims (12, 13, 14, 15, 16)
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17. A system for using a hybrid predictive model to respond to search queries for customer support content in a question and answer customer support system, the system comprising:
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at least one processor; and at least one memory coupled to the at least one processor, the at least one memory having stored therein instructions which, when executed by any set of the one or more processors, perform a process for using a hybrid predictive model to respond to search queries for customer support content in a question and answer customer support system, the process including; storing existing customer support content data representing existing customer support content entries, the existing customer support content entries including groups of combinations of existing search queries and existing responses, the existing search queries having been submitted to the question and answer customer support system by prior users and the existing responses having been submitted to a question and answer customer support system in response to the existing search queries; receiving search query data from a user, the search query data representing a current search query for customer support content from the question and answer customer support system; training a hybrid predictive model through unsupervised learning using at least a combination of machine learning content and crowdsourced user feedback to identify the customer support content of the current search query, resulting in hybrid predictive model data, the training at least including applying one or more additional natural language processing algorithms to the crowdsourced user feedback to extract one or more features of the customer support content from the user feedback, the training including consideration of a length of reference material and a style of a reference material; applying the search query data to the predictive model data to generate content selection score data representing two or more content selection scores, at least one of the two or more content selection scores representing a relevance of the existing customer support content entries to the search query data, at least one of the two or more content selection scores representing a quality of the customer support content, the quality-related content selection score being provided by a content quality predictive model; selecting one of the existing customer support content entries at least partially based on the one or more content selection scores; generating user experience display data that includes at least one user experience page and that includes the one of the existing customer support entries, the user experience display data representing a user experience display that renders the one of the existing customer support content entries in the at least one user experience page; providing the user experience display data to the user, in response to receiving the search query data from the user; requesting user feedback data from the user, to enable the question and answer customer support system to improve performance of the hybrid predictive model, the user feedback data representing crowdsourced user feedback related to the one of the existing customer support content entries that is included in the user experience display data; and updating, upon receipt of user feedback data from the user, the hybrid predictive model at least partially based on the user feedback data, the received feedback including data regarding a quality of writing, perceived accuracy or content and search ranking. - View Dependent Claims (18, 19, 20, 21, 22, 23)
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Specification