Concept-level user intent profile extraction and applications

Abstract

Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

1 . A computer-implemented method comprising:
 identifying, via a processor, structured user data from a social media site, the structured user data comprising an email identification, phone number, geo-location, friends and links;   obtaining a global concept graph comprising nodes that are concepts, and edges that are relationships among such concepts, wherein the concepts comprise phrases that represent entities, domain-specific terms and common expressions that are used to convey information;   wherein the relationships are identified by annotated edges among concepts, wherein the relationships comprise measures of closeness among the concepts;   obtaining at least one weighted sub-graph of the global concept graph using the structured user data, user activities on the social media site, the user activities not involving the social media site, and user searches that do not involve the social media site;   obtaining an intent profile of the user from information in the weighted sub-graph(s); and   matching the intent profile with an advertiser profile to target an advertisement to the user.   
     
     
         2 . The method of  claim 1  further comprising identifying, via the processor, user activities involving the Internet that do not involve the social media site. 
     
     
         3 . The method of  claim 1  further comprising:
 generating a score for each vertical in a targeting vertical list. 
 
     
     
         4 . The method of  claim 3 , wherein the score comprises a time factor and an interest factor. 
     
     
         5 . The method of  claim 3 , wherein the score is generated based on user activities on the social media site, user activities involving the Internet, incoming likes, sharing, recommendation on the social media site, and user searches. 
     
     
         6 . The method of  claim 1  further comprising modifying a user experience of the user by organizing a contact list of the user and conversations and posts of the user based on their content and conceptual context. 
     
     
         7 . The method of  claim 6 , further comprising performing an aggregation scoring. 
     
     
         8 . The method of  claim 1 , wherein the concepts are selected from the group consisting of people, companies, drugs, diets, films, shows, events, wherein the domain-specific terms are selected from the group consisting of sports and medical terminologies, specific treatments, and procedures. 
     
     
         9 . The method of  claim 1 , further comprising tagging the intent profile of the user with temporal data. 
     
     
         10 . The method of  claim 1 , further comprising:
 tagging unstructured data using collective activities of users at the social media site,   wherein the unstructured data is selected from searches, we-browsing, posts, comments, content of web pages that received likes and links.   
     
     
         11 . The method of  claim 1 , further comprising prioritizing unstructured data using collective activities of users at the social media site. 
     
     
         12 . The method of  claim 1 , further comprising post-processing and tagging the intent profile of the user with weighted category scores defined over a structured taxonomy of interest. 
     
     
         13 . The method of  claim 1 , further comprising assigning at least one advertiser a set of advertiser target profiles. 
     
     
         14 . The method of  claim 13 , wherein each of the advertiser target profiles comprises a weighted list of categories picked from a structured taxonomy. 
     
     
         15 . The method of  claim 1 , wherein selecting a final set of advertisement units is completed by an optimization process that maximizes objective functions of interest, including revenue for the social media site, value and Return-On-Investment (ROI) for advertisers, while considering a device and media of the user. 
     
     
         16 . The method of  claim 6 , wherein modifying a user experience comprises organizing friends or contact lists of the user into potentially overlapping groups by computing similarity between a user's profile and the profiles of those of the user's friends and contacts. 
     
     
         17 . The method of  claim 16 , wherein organizing friends or contact lists of a user into potentially overlapping groups comprise computing similarity between a user's profile and the profiles of those of the user's friends and contacts. 
     
     
         18 . The method of  claim 6 , wherein modifying a user engagement comprises organizing posts, comments and social interactions between a user and his friends based on an underlying context. 
     
     
         19 . A non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to perform:
 identifying structured user data from a social media site, the structured user data comprising an email identification, phone number, geo-location, friends and links;   obtaining a global concept graph comprising nodes that are concepts, and edges that are relationships among such concepts, wherein the concepts comprise phrases that represent entities, domain-specific terms and common expressions that are used to convey information;   wherein the relationships are identified by annotated edges among concepts, wherein the relationships comprise measures of closeness among the concepts;   obtaining at least one weighted sub-graph of the global concept graph using the structured user data; user activities on the social media site; the user activities not involving the social media site; the incoming likes, sharing, and recommendations on the social media site; and user searches that do not involve the social media site;   obtaining an intent profile of the user from information in the weighted sub-graph(s); and   matching the intent profile with an advertiser profile to target an advertisement to the user.   
     
     
         20 . A system comprising:
 a processor; and   memory communicably coupled to the processor;   wherein the processor:   identifies structured user data from a social media site, the structured user data comprising an email identification, phone number, geo-location, friends and links;   identifies incoming likes, sharing, recommendations on the social media site;   obtains a global concept graph comprising nodes that are concepts, and edges that are relationships among such concepts, wherein the concepts comprise phrases that represent entities, domain-specific terms and common expressions that are used to convey information;   wherein the relationships are identified by annotated edges among concepts, wherein the relationships comprise measures of closeness among the concepts;   obtains at least one weighted sub-graph of the global concept graph using the structured user data; user activities on the social media site; the user activities not involving the social media site; and user searches that do not involve the social media site;   obtains an intent profile of the user from information in the weighted sub-graph(s); and   matches the intent profile with an advertiser profile to target an advertisement to the user.