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Method of recommending news articles based on users' social network connections Disclosure Number: IPCOM000198136D
Publication Date: 2010-Jul-26
Document File: 2 page(s) / 262K

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Described is a method to access a user’s existing social network structures to find the news articles that these persons are accessing and provide each user with a list of articles being read by the persons associated with them through their social network structures. The method will sort the list of recommended articles three ways: Ordered by the total number of times each article is read; based on the date that the articles were read; and a combination of the two previous sorts. The user or the implementer of the method will be able to decide which sorting methodology will be used.

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Method of recommending news articles based on users ' social network connections

A problem with today's news websites is that they contain a large number of articles out of which only a small proportion are of interest to each reader. Every user has to go through a vast number of articles and perform a number of search queries in order to find the information that is of interest. A solution to this problem is a method that is able to recommend news articles to each user based on their personal interests by utilizing their existing social network structures.

The invention utilizes any of the reader's existing social network structures to make news article recommendations that are personalized to the user. This is achieved by monitoring and keeping track of the news articles that the persons associated with the reader through their social networks are reading. By ranking the articles based on the number of times they have been read and taking the highest scoring ones the implementation can present them to the user as recommended news articles. The philosophy behind the invention is that members of a social network that are connected to each other will most probably share some common interests or functions so they must be interested in the same news.

Figure 1 represents how the recommended articles are displayed, using the Google News website as an example.

Figure 1: Display in Google News

The benefit of the invention is that using minimal processing power the user...