Abstract: The distribution of content items, such as news items, in a news publishing platform is governed by a plurality of interrelated factors. These factors include publisher bias, trust bias, and user-specific bias, which reflect the user's reading history, the social clusters to which the user belongs, the user's location etc. To model the relevance of each content item to a given user, a metric of the overall story strength is calculated in accordance with one or more of these biases. The content item is delivered to the target in accordance with the story strength metric.

Abstract: The distribution of content items, such as news items, is dependent in part upon the geographic relevance of the content item and the geographic community to which the target reader belongs. The geographic relevance of content items is determined and compared to a geographic profile for the reader at the distribution target. Where the function of geographic relevance exceeds a predetermined threshold, the content item is delivered to the target. As a result the reader is presented with content items that are more closely relevant to his geographic profile and desired interest threshold.

Abstract: The distribution of content items, such as news items, is dependent in part upon the geographic relevance of the content item and the geographic community to which the target reader belongs. The geographic relevance of content items is determined and compared to a geographic profile for the reader at the distribution target. Where the function of geographic relevance exceeds a predetermined threshold, the content item is delivered to the target. As a result the reader is presented with content items that are more closely relevant to his geographic profile and desired interest threshold.

Abstract: The distribution of content items, such as news items, in a news publishing platform is governed by a plurality of interrelated factors. These factors include publisher bias, trust bias, and user-specific bias, which reflect the user's reading history, the social clusters to which the user belongs, the user's location etc. To model the relevance of each content item to a given user, a metric of the overall story strength is calculated in accordance with one or more of these biases. The content item is delivered to the target in accordance with the story strength metric.