Digital content personalization method and system
First Claim
1. A method of producing and storing a content interest profile, said method comprising:
- using a communications interface receiving from the user a request for service of one or more content items;
receiving and storing a unique identifier, URL content, time period, and IP address of the user;
using a processor device configured to operate as;
a user profile generator tracking digital content consumed by a user and user interaction with said digital content by performing steps of;
determining whether the user has been provided the unique identifier (ID);
if the user has not been provided the unique identifier, assigning the unique identifier to the user;
placing software code in the user'"'"'s web site to read and record a URL content (uniform resource locator) of web pages or other address from which digital content can be retrieved;
storing the unique identifier in a database; and
using page-based JavaScript code to report the interaction in real-time via an application programming interface; and
constructing a detailed profile of the user'"'"'s interests based on the digital content consumed and the interaction with the digital content;
using a processor device configured to operate as;
a content analysis and data mining engine performing collaborative filtering, comprising processing the digital content through data-mining, semantic filters, and content enrichment processing to extract topics, concepts, and entities related to each content item;
assigning a relevance score to the processed digital content, wherein said relevance score is based on frequency, content relevance, collaborative filtering scores, social tags, user actions, prior domain knowledge, a measure of recency, editorial rules, and a commercial value of the content;
storing the relevance score;
ranking recommendable content based on the relevance score;
using a processor device configured to operate as;
a content recommendation engine determining what digital content is likely to be of interest to the user by matching the user'"'"'s interests from the profile built by the user profile generator with an available pool of content to find a closest match to the user'"'"'s interest, factoring in relevance, recency of the digital content based on either its initial publication or subsequent updates, popularity, and an interest of other similar users.
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Abstract
A system and method for predicting what content a user wants to view based on such user'"'"'s previous behavior and actions, comprising: receiving a cookie for every content page template in a web site; receiving a request for service of a content page; sending the content requested to a requester; for each content page sent, retrieving the cookie from the user; assigning a unique identifier (ID) to each new requester and storing the ID in the cookie; recording each ID, IP address, referrer, and time of request from the server; and storing the data recorded in a buffer for a period of time before storing it more permanently in a client-specific database. The system can be monetized by receiving fees from end users for presenting the content preferences or by receiving fees form content providers that include advertising related to the content preferences.
92 Citations
24 Claims
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1. A method of producing and storing a content interest profile, said method comprising:
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using a communications interface receiving from the user a request for service of one or more content items; receiving and storing a unique identifier, URL content, time period, and IP address of the user; using a processor device configured to operate as; a user profile generator tracking digital content consumed by a user and user interaction with said digital content by performing steps of; determining whether the user has been provided the unique identifier (ID); if the user has not been provided the unique identifier, assigning the unique identifier to the user; placing software code in the user'"'"'s web site to read and record a URL content (uniform resource locator) of web pages or other address from which digital content can be retrieved; storing the unique identifier in a database; and using page-based JavaScript code to report the interaction in real-time via an application programming interface; and constructing a detailed profile of the user'"'"'s interests based on the digital content consumed and the interaction with the digital content; using a processor device configured to operate as; a content analysis and data mining engine performing collaborative filtering, comprising processing the digital content through data-mining, semantic filters, and content enrichment processing to extract topics, concepts, and entities related to each content item; assigning a relevance score to the processed digital content, wherein said relevance score is based on frequency, content relevance, collaborative filtering scores, social tags, user actions, prior domain knowledge, a measure of recency, editorial rules, and a commercial value of the content; storing the relevance score; ranking recommendable content based on the relevance score; using a processor device configured to operate as; a content recommendation engine determining what digital content is likely to be of interest to the user by matching the user'"'"'s interests from the profile built by the user profile generator with an available pool of content to find a closest match to the user'"'"'s interest, factoring in relevance, recency of the digital content based on either its initial publication or subsequent updates, popularity, and an interest of other similar users. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 23, 24)
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13. A method of applying statistical, probabilistic, and predictive methods to data contained in a user'"'"'s profile in order to determine the likely interest of a user in content not yet viewed, said method comprising steps of:
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using an information processing device for; using a plurality of calculations to determine a pattern in a subject matter of articles previously viewed by determining similarities in substance between articles previously viewed and a set of articles not yet viewed, said determining comprising; comparing a plurality of data points, wherein said data points comprise a content'"'"'s author, source, time of creation, time of publication, and length; wherein the length is measured in terms of character, word or paragraph count for text assets; selecting content most likely to be of interest to the user by using a matching function that is performed based on recency of the content based on either its initial publication or subsequent updates; and ranking recommendable assets based on a series of signals including the content relevance, collaborative filtering scores, social tags, user actions, prior domain knowledge, a measure of recency, editorial rules and the commercial value of the asset. - View Dependent Claims (14, 15)
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16. A method for making personalized content recommendations to users, said method comprising:
using an information processing device for; scoring each available content asset against a content preference of each user contained in a user profile; applying statistical, probabilistic and predictive methods to data contained in a user'"'"'s profile in order to determine a likely interest of a user in content not yet viewed; and ranking recommendable content assets based on a series of signals including the content relevance, collaborative filtering scores, social tags, user actions, prior domain knowledge, a measure of recency, editorial rules and the commercial value of the asset. - View Dependent Claims (17, 18, 19, 20, 21, 22)
Specification