Statistical personalized recommendation system

A method for identifying similar users comprising:

• maintaining a history of ratings of the items by users in a group of users;
• computing parameters using the history of ratings, said parameters being associated with the group of users and enabling computation of a predicted rating of any of the items by an unspecified user in the group;
• computing personalized statistical parameters for each of one or more individual users in the group using the parameters associated with the group and the history of ratings of the items by that user, said personalized parameters enabling computation of a predicted rating of any of the items by that user;
• identifying similar users to a first user using the computed personalized statistical parameters for the users.

Software stored on computer readable media comprising instructions for causing a computer system to perform functions comprising:

• maintaining user-related data including storing a history of ratings of items by users in one or more groups of users;
• computing parameters associated with the one or more groups using the user-related data, including for each of the one or more groups of users computing parameters characterizing predicted ratings of items by users in the group;
• computing personalized statistical parameters for each of one or more individual users using the parameters associated with said user's group of users and the stored history of ratings of items by that user;
• computing predicted ratings of the items by the each of one or more users using the personalized statistical parameters.

Software stored on computer readable media comprising instructions for causing a computer system to perform functions comprising:

• maintaining a history of ratings of the items by users in a group of users;
• computing parameters using the history of ratings, said parameters being associated with the group of users and enabling computation of a predicted rating of any of the items by an unspecified user in the group;
• computing personalized statistical parameters for each of one or more individual users in the group using the parameters associated with the group and the history of ratings of the items by that user, said personalized parameters enabling computation of a predicted rating of any of the items by that user;
• identifying similar users to a first user using the computed personalized statistical parameters for the users.