Abstract: A method and system provide the ability to personalize a digital channel. Multiple content assets are obtained and include an image content asset. Each of the assets is associated with an associated set of semantic elements. The multiple content assets are clustered into content clusters based on a similarity of the semantic elements. A first content asset is selected. The clustering is used as a metric to estimate distances between the first content asset and remaining multiple content assets. The remaining multiple content assets are scored based on the distances. One of the remaining multiple content assets is selected based on the scoring and provided for a personalized component of the digital channel. In addition, a coverage map that includes both users and content may be generated based on the clusters and then utilized to select the content asset.

Abstract: A method, system, and apparatus provide the ability to personalize a digital channel. A digital channel is provided to multiple users and visitor information at each visit is collected. The visitor information includes data about each visit and multiple content items that are presented. The users are autonomously clustered by segmenting the user population into behavioral groups such that mutual information is maximized between the users in an assigned behavioral group and the content items. Based on the clustering, a model is generated that estimates a score for each interaction between users and content items. The model is updated at a defined interval. Based on the score, content items to recommend to a specific user are determined. The recommendation jointly maximizes an outcome and a learning speed of the model. The personalized digital channel is delivered to the specific user based on the recommended multiple content items.

Abstract: A method and system provide the ability to personalize a digital channel. Multiple content assets are obtained and include an image content associate. Each of the assets is associated with an associated set of semantic elements. The multiple content assets are clustered into content clusters based on a similarity of the semantic elements. A first content asset is selected. The clustering is used as a metric to estimate distances between the first content asset and remaining multiple content assets. The remaining multiple content assets are scored based on the distances. One of the remaining multiple content assets is selected based on the scoring and provided for a personalized component of the digital channel. In addition, a coverage map that includes both users and content may be generated based on the clusters and then utilized to select the content asset.