Data clustering and user modeling for next-best-action decisions
First Claim
1. A method for data clustering and user modeling for next-best-action decisions, the method comprising the computer-implemented steps of:
- receiving unstructured social data of a plurality of users, the unstructured social data comprising one or more indicators including a set of words located in the unstructured social data that indicate at least one of;
sentiment, personality, and emotional state;
analyzing the unstructured social data of each user of the plurality of users to assign a numerical value to each of a plurality of feature vectors to associate with each of the plurality of users based on the set of words of the one or more indicators located in the unstructured social data generated by the user, each of the set of feature vectors corresponding to one or more personality characteristics that include a learning style, a propensity to purchase, a socioeconomic class, and a personality trait of each of the plurality of users;
analyzing the set of feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors;
grouping the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster;
identifying attributes of the cluster based on the feature vectors of the users grouped in the cluster; and
inputting the attributes of the cluster into a predictive model to automatically determine a commercial offer that corresponds to the cluster.
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Abstract
Embodiments herein provide data clustering and user modeling for next-best-action decisions. Specifically, a modeling tool is configured to: receive indicators within unstructured social data from a plurality of users; analyze the unstructured social data of each of the plurality of users to assign a set of feature vectors to each of the plurality of users, each feature vector corresponding to one or more personality characteristics of each of the plurality of users; and analyze the feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors. The modeling tool is further configured to: group the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster; identify attributes of the cluster; and input the attributes of the cluster into a predictive model to determine an offer corresponding to the cluster.
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Citations
20 Claims
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1. A method for data clustering and user modeling for next-best-action decisions, the method comprising the computer-implemented steps of:
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receiving unstructured social data of a plurality of users, the unstructured social data comprising one or more indicators including a set of words located in the unstructured social data that indicate at least one of;
sentiment, personality, and emotional state;analyzing the unstructured social data of each user of the plurality of users to assign a numerical value to each of a plurality of feature vectors to associate with each of the plurality of users based on the set of words of the one or more indicators located in the unstructured social data generated by the user, each of the set of feature vectors corresponding to one or more personality characteristics that include a learning style, a propensity to purchase, a socioeconomic class, and a personality trait of each of the plurality of users; analyzing the set of feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors; grouping the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster; identifying attributes of the cluster based on the feature vectors of the users grouped in the cluster; and inputting the attributes of the cluster into a predictive model to automatically determine a commercial offer that corresponds to the cluster. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer system for data clustering and user modeling for next-best-action decisions, the system comprising:
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at least one processing unit; memory operably associated with the at least one processing unit; and a modeling tool storable in memory and executable by the at least one processing unit, the modeling tool comprising; an analyzing component configured to; receive unstructured social data from a plurality of users, the unstructured social data comprising one or more indicators including a set of words located in the unstructured social data that indicate at least one of;
sentiment, personality, and emotional state;analyze the unstructured social data of each user of the plurality of users to assign a numerical value to each of a plurality of feature vectors to associated with each of the plurality of users based on the set of words of the one or more indicators located in the unstructured social data generated by the user, each of the set of feature vectors corresponding to one or more personality characteristics that include a learning style, a propensity to purchase, a socioeconomic class, and a personality trait of each of the plurality of users; and analyze the set of feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors; a clustering component configured to; group the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster; and identify attributes of the cluster based on the feature vectors of the users grouped in the cluster; and an offering component configured to input the attributes of the cluster into a predictive model to automatically determine a commercial offer that corresponds to the cluster. - View Dependent Claims (10, 11, 12, 13, 14)
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15. A computer-readable storage device storing computer instructions, which when executed, enables a computer system to provide data clustering and user modeling for next-best-action decisions, the computer instructions comprising:
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receiving unstructured social data of a plurality of users, the unstructured social data comprising one or more indicators including a set of words located in the unstructured social data that indicate at least one of;
sentiment, personality, and emotional state;analyzing the unstructured social data of each user of the plurality of users to assign a numerical value to each of a plurality of feature vectors to associated with each of the plurality of users based on the set of words of the one or more indicators located in the unstructured social data generated by the user, each of the set of feature vectors corresponding to one or more personality characteristics that include a learning style, a propensity to purchase, a socioeconomic class, and a personality trait of each of the plurality of users; analyzing the set of feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors; grouping the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster; identifying attributes of the cluster; and inputting the attributes of the cluster into a predictive model to automatically determine a commercial offer that corresponds to the cluster. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification