Abstract: In a data processing system, a method of recommending articles and products to a user is disclosed. The method creates a frequency vector in relation to the content of an article, frequency vectors in relation each of one or more products from intermediate data. The method compares the vectors to determine a content similarity measure, and provides as output a list of one or more products having the highest content similarity measures. The method may also determine a correlation measure. An electronic data processing system for recommending articles and products to a user is also disclosed. The system includes modules to receive article information and product information, a correlation module to determine a content similarity measure between the article and each of the products and, a multiplexer module for providing a list comprising the article and the products associated having the highest content similarity measure.

Abstract: A system and method to classify web-based documents as articles or non-articles is disclosed. The method generates a machine learning model from a human labelled training set which contains articles and non-articles. The machine learning model is applied to new articles to label them as articles or non-articles. The method generates the machine learning model based on content, such as text and tags of the web-based documents. The invention also provides for devices which incorporate the machine learning model, allowing such devices to classify documents as articles or non-articles.

Abstract: A system and method for recommending on-line articles and documents to users is disclosed. The method provides an article widget user interface and a full-screen widget user interfaces to allow a user to rate articles, to preview articles, to filter articles based on category, article length, or other characteristics. A recommender system is configured to provide a continually refreshing list of recommended articles to the user via the user interfaces. The system comprises a module configured to monitor the user's explicit and implicit interactions with the user interfaces, and provides a refreshed list of recommended articles accordingly. The recommender system may be configured to use a package of approaches including rule-based, content-based or collaborative filtering approaches including Slope, Co-Visitation, Mwinnow and Clustering/Co-clustering.