Collaborative filtering

A computer program product, encoded on a tangible program carrier, operable to cause data processing apparatus to perform operations comprising:

• clustering a plurality of users with respect to one or more latent variables in a probability distribution model of a relationship between a set of users and a set of items, the probability distribution model comprising a probability distribution of the set of items with respect to the latent variables;
• as new items are added to the set of items, updating the probability distribution of a current set of items with respect to the latent variables including determining counter values incremented in response to item selections in the current set of items and according to user membership in categories represented by the latent variables and where updating the probability distribution includes calculating, for each category, a fraction of counts for an item relative to all the counter values for items in the particular category;
• and
• generating a relationship score for one or more users with respect to the set of items, each relationship score relating the particular user to a particular item based on the particular user's category memberships and based on the updated probability distribution of the set of the items with respect to the latent variables.

A system comprising:

• one or more computers configured to perform operations including:
• clustering a plurality of users with respect to one or more latent variables in a probability distribution model of a relationship between a set of users and a set of items, the probability distribution model comprising a probability distribution of the set of items with respect to the latent variables;
• as new items are added to the set of items, updating the probability distribution of a current set of items with respect to the latent variables including determining counter values incremented in response to item selections in the current set of items and according to user membership in categories represented by the latent variables and where updating the probability distribution includes calculating, for each category, a fraction of counts for an item relative to all the counter values for items in the particular category;
• and
• generating a relationship score for one or more users with respect to the set of items, each relationship score relating the particular user to a particular item based on the particular user's category memberships and based on the updated probability distribution of the set of the items with respect to the latent variables.

A computer-implemented method comprising:

• identifying data including a set of users that includes as members a plurality of users and a set of items that includes as members a plurality of user selectable items;
• calculating an initial overall probability distribution using the set of users, the set of items, and a set of categories representing latent variables, where the overall probability distribution includes a first probability distribution relating the set of users and the set of categories and a second probability distribution relating the set of items and the set of categories;
• determining a new second probability distribution relating an updated set of items and the set of categories using selection data relating item selections from users of the set of users and the categories associated with the users;
• calculating an overall probability distribution relating users of the set of users and items of the updated set of items using the first probability distribution and the new second probability distribution;
• and
• using the overall probability distribution to recommend one or more items to one or more users of the set of users.

A computer program product, encoded on a tangible program carrier, operable to cause data processing apparatus to perform operations comprising:

• identifying data including a set of users that includes as members a plurality of users and a set of items that includes as members a plurality of user selectable items;
• calculating an initial overall probability distribution using the set of users, the set of items, and a set of categories representing latent variables, where the overall probability distribution includes a first probability distribution relating the set of users and the set of categories and a second probability distribution relating the set of items and the set of categories;
• determining a new second probability distribution relating an updated set of items and the set of categories using selection data relating item selections from users of the set of users and the categories associated with the users;
• calculating an overall probability distribution relating users of the set of users and items of the updated set of items using the first probability distribution and the new second probability distribution;
• and
• using the overall probability distribution to recommend one or more items to one or more users of the set of users.

A system comprising:

• one or more computers operable to:
• identify data including a set of users that includes as members a plurality of users and a set of items that includes as members a plurality of user selectable items;
• calculate an initial overall probability distribution using the set of users, the set of items, and a set of categories representing latent variables, where the overall probability distribution includes a first probability distribution relating the set of users and the set of categories and a second probability distribution relating the set of items and the set of categories;
• determine a new second probability distribution relating an updated set of items and the set of categories using selection data relating item selections from users of the set of users and the categories associated with the users;
• calculate an overall probability distribution relating users of the set of users and items of the updated set of items using the first probability distribution and the new second probability distribution;
• and
• use the overall probability distribution to recommend one or more items to one or more users of the set of users.