Digital content personalization method and system

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.