DOMAIN GENERIC LARGE SCALE TOPIC EXPERTISE AND INTEREST MINING ACROSS MULTIPLE ONLINE SOCIAL NETWORKS
The system relates to a system and apparatus for a scalable engineering system deployed in production that mines topical interests from multiple social networks and assigns over tens of thousands of topics to hundreds of millions of users on a daily basis. The system extracts and analyzes features for topic inference that extend beyond authored text. The system uses a diverse set of features and cross network information can lead to a better understanding of a user's interests. This system focuses on assigning topics for a user that other users can socially recognize and acknowledge.
1 . A computed-implemented system for mining expertise and interest topics of social network users across a plurality of computer-based social networks and external data base sources that can be used to generate profit-optimal resource allocations for communication to a social network user, the system comprising:
a computer data store containing:
a plurality of external data base sources containing social network user topics of interest data for the social network user;
a dictionary of topics of interest data phrases to be extracted from the social network and from the external data base sources for the social network user topics of interest data;
a computer server coupled to the computer store and programmed to:
identify social network user topics of interest data associated with the social network user contained on the plurality of social networks;
retrieve the topics of interest data phrases from the social networks and from the external data base sources using the topics of interest data in the dictionary using a test feature extraction function wherein the text extraction feature comprises extracting topics of interest data based on a user profile indicating a user's interest, a user's activities and a user's connections;
map the topics of interest data by assigning them to the social network user using a domain feature mapping function that interacts with the text feature extraction function wherein the domain feature mapping function comprises topic feature generation and attribution for the user;
predict topics of interest for the user based on the topic feature generation and attribution; and
use the predicted topics of interest for the user to generate promotional messages to be sent to the user.
2 . The system of claim 1 wherein the attribution for the user denotes the relationship of the input source to the user selected from the group consisting of:
user generated content;
actor generated content generated by a second user in reaction to the user generated content;
credited content which has no direct association with the user; and
social graph generated content generated from topics of interest of other users with which the user has a relationship.
3 . The system of claim 1 wherein supervised learning is used to predict the topics of interest for the user.
4 . The system of claim 1 wherein the promotional messages are perk targeting.
5 . The system of claim 1 wherein the promotional messages contain content comprising articles of interest to the user.
6 . A computer-implemented system useful for a commercial enterprise to target promotional messages to be sent to social network users, the system comprising:
a computer data store containing:
a plurality of external data base sources containing social network user topics of interest data for the social network user;
a dictionary of topics of interest data phrases to be extracted from a computer-based social network and from the external data base sources for the social network user topics of interest data;
a computer server coupled to the computer store and programmed to:
identify social network user topics of interest data associated with the social network user contained on the plurality of computer-based social networks;
retrieve the topics of interest data phrases social networks and from the external data base sources using the topics of interest data in the dictionary using a test feature extraction function wherein the text extraction feature comprises extracting topics of interest data based on a user profile indicating a user's interest, a user's activities and a user's connections;
map the topics of interest data by assigning them to the social network user using a domain feature mapping function that interacts with the text feature extraction function wherein the domain feature mapping function comprises topic feature generation and attribution for the user;
predict topics of interest for the user based on the topics feature generation and attribution; and
use the predicted topics of interest for the user to generate promotional messages to be sent to the user.
7 . The system of claim 6 wherein supervised learning is used to predicting the topics of interest for the user.
8 . The system of claim 6 wherein the promotional messages are perk targeting.
9 . The system of claim 6 wherein the promotional messages contain content comprising articles of interest to the user.
10 . A non-transitory computer-readable medium with instructions store thereon, that when executed by a processor, perform the steps comprising:
using a plurality of external data base sources hosted on a computer data store containing the social network user topics of interest data for the social network user;
using a dictionary of topics of interest data phrases to be extracted from a computer-based social network and from the external data base sources for the social network user topics of interest data;
identifying social network user topics of interest data associated with the social network user contained on the plurality of computer-based social networks;
retrieving the topics of interest data phrases social networks and from the external data base sources using the topics of interest data in the dictionary using a test feature extraction function wherein the text extraction feature comprises extracting topics of interest data based on a user profile indicating a user's interest, a user's activities and a user's connections;
mapping the topics of interest data by assigning them to the social network user using a domain feature mapping function that interacts with the text feature extraction function wherein the domain feature mapping function comprises topic feature generation and attribution for the user;
predicting topics of interest for the user based on the topic feature generation and attribution; and
using the predicted topics of interest for the user to generate promotional messages to be sent to the user.