System and method for personalized search, information filtering, and for generating recommendations utilizing statistical latent class models

Claim 1

A method in a computer system for generating a recommendation list of desired items from a set of data including at least one of: items, content descriptors for the items, user profiles about transactions, prior searches, user ratings and user actions, to generate a recommendation list of desired items, comprising the steps of: statistically analyzing the set of data to learn semantic associations between words within specific items of the set of data, storing the learned semantic associations in computer hardware; computing probabilities of each learned semantic association; receiving into the computer system for generating a recommendation list: an actual user profile, a user query; and a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being the desired items; computing a probability of relevance of each item in the set of data to the actual user profile and said user query, the step of computing a probability of relevance including combining the learned semantic associations and the actual user profile; and generating at least one recommendation list of desired items.

Claim 2

A method in a computer system for generating a recommendation list of desired items from a set of data including at least one of: items, content descriptors for the items, user profiles about transactions, prior searches, user ratings and user actions, to generate a recommendation list of desired items, comprising the steps of: statistically analyzing the set of data to learn semantic associations between words within specific items of the set of data, storing the learned semantic associations in computer hardware; computing probabilities of each learned semantic association; receiving into the computer system for generating a recommendation list: an actual user profile, a user query; and a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being the desired items; computing a probability of relevance of each item in the set of data to the actual user profile and said user query, the step of computing a probability of relevance including combining the learned semantic associations and the actual user profile; and generating at least one recommendation list of desired items.

Claim 3

A method in a computer system containing a recommendation system for a set of data including items, content descriptors for the items, user profiles about transactions, prior searches, user ratings or user actions, to generate a recommendation list of desired items, comprising the following steps: receiving into the recommendation system a set of statistical latent class models along with appropriate model combination weights, each possible combination of items, content descriptors, users, object attributes or user attributes, and preferences being assigned a probability indicating the likelihood of that particular combination, storing the set of statistical latent class models in computer hardware; receiving into the recommendation system at least one of: an actual user profile; and a user query; receiving into the recommendation system a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being desired items when said user query is not received into the recommendation system, generating a user query; computing a probability of relevance for each item in the set of data utilizing the received set of statistical latent class models and the set of data, the step of computing a probability of relevance includes accounting for a temporal structure of transactions, ratings and user actions; and computing the probabilities of relevance of items to a user for a particular point in time; and generating at least one recommendation list, each recommendation list having a variable length and consisting of a ranked list of desired items, the items being ranking based on the computed probability of relevance.

Claim 4

A method in a computer system containing a recommendation system for a set of data including items, content descriptors for the items, user profiles about transactions, prior searches, user ratings or user actions, to generate a recommendation list of desired items, comprising the following steps: receiving into the recommendation system a set of statistical latent class models along with appropriate model combination weights, each possible combination of items, content descriptors, users, object attributes or user attributes, and preferences being assigned a probability indicating the likelihood of that particular combination, storing the set of statistical latent class models in computer hardware; receiving into the recommendation system at least one of: an actual user profile; and a user query; receiving into the recommendation system a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being desired items when said user query is not received into the recommendation system, generating a user query; computing a probability of relevance for each item in the set of data utilizing the received set of statistical latent class models and the set of data, the step of computing a probability of relevance includes accounting for a temporal structure of transactions, ratings and user actions; and computing the probabilities of relevance of items to a user for a particular point in time; and generating at least one recommendation list, each recommendation list having a variable length and consisting of a ranked list of desired items, the items being ranking based on the computed probability of relevance.

Claim 5

A method in a computer system containing a recommendation system for a set of data including items, content descriptors for the items, user profiles about transactions, prior searches, user ratings or user actions, to generate a recommendation list of desired items, comprising the following steps: receiving into the recommendation system a set of statistical latent class models along with appropriate model combination weights, each possible combination of items, content descriptors, users, object attributes or user attributes, and preferences being assigned a probability indicating the likelihood of that particular combination, storing the set of statistical latent class models in computer hardware; receiving into the recommendation system at least one of: an actual user profile; and a user query; receiving into the recommendation system a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being desired items; when said user query is not received into the recommendation system, generating a user query; computing a probability of relevance for each item in the set of data utilizing the received set of statistical latent class models and the set of data, the step of computing a probability of relevancy includes computing expected utility to the user using the probability of relevance for each of the items; and generating at least one recommendation list, each recommendation list having a variable length and consisting of a ranked list of desired items, the items being ranking based on the computed probability of relevance.

Claim 6

A method in a computer system containing a recommendation system for a set of data including items, content descriptors for the items, user profiles about transactions, prior searches, user ratings or user actions, to generate a recommendation list of desired items, comprising the following steps: receiving into the recommendation system a set of statistical latent class models along with appropriate model combination weights, each possible combination of items, content descriptors, users, object attributes or user attributes, and preferences being assigned a probability indicating the likelihood of that particular combination, storing the set of statistical latent class models in computer hardware; receiving into the recommendation system at least one of: an actual user profile; and a user query; receiving into the recommendation system a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being desired items; when said user query is not received into the recommendation system, generating a user query; computing a probability of relevance for each item in the set of data utilizing the received set of statistical latent class models and the set of data, the step of computing a probability of relevancy includes computing expected utility to the user using the probability of relevance for each of the items; and generating at least one recommendation list, each recommendation list having a variable length and consisting of a ranked list of desired items, the items being ranking based on the computed probability of relevance.

Claim 10

A method in a computer system containing a recommendation system for a set of data including items, content descriptors for the items, user profiles about transactions, prior searches, user ratings or user actions, to generate a recommendation list of desired items, comprising the following steps: generating a set of statistical latent class models by probabilistic latent semantic indexing and storing the set of statistical latent class models in computer hardware; receiving into the recommendation system the set of statistical latent class models along with appropriate model combination weights, each possible combination of items, content descriptors, users, object attributes and user attributes, and preferences being assigned a probability indicating the likelihood of that particular combination; receiving into the recommendation system at least one of: an actual user profile; and a user query; receiving into the recommendation system a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being desired items; when said user query is not received into the recommendation system, generating a user query; computing a probability of relevance for each item in the set of data utilizing the received set of statistical latent class models and the set of data; and generating at least one recommendation list, each recommendation list having a variable length and consisting of a ranked list of desired items, the items being ranking based on the computed probability of relevance.

Claim 11

A method in a computer system containing a recommendation system for a set of data including items, content descriptors for the items, user profiles about transactions, prior searches, user ratings or user actions, to generate a recommendation list of desired items, comprising the following steps: generating a set of statistical latent class models by probabilistic latent semantic indexing and storing the set of statistical latent class models in computer hardware; receiving into the recommendation system the set of statistical latent class models along with appropriate model combination weights, each possible combination of items, content descriptors, users, object attributes and user attributes, and preferences being assigned a probability indicating the likelihood of that particular combination; receiving into the recommendation system at least one of: an actual user profile; and a user query; receiving into the recommendation system a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being desired items; when said user query is not received into the recommendation system, generating a user query; computing a probability of relevance for each item in the set of data utilizing the received set of statistical latent class models and the set of data; and generating at least one recommendation list, each recommendation list having a variable length and consisting of a ranked list of desired items, the items being ranking based on the computed probability of relevance.

Claim 20

A method in a computer system containing a recommendation system for a set of data including items, content descriptors for the items, user profiles about transactions, prior searches, user ratings or user actions, to generate a recommendation list of desired items, comprising the following steps: receiving into the recommendation system a set of statistical latent class models along with appropriate model combination weights, each possible combination of items, content descriptors, users, object attributes and user attributes, and preferences being assigned a probability indicating the likelihood of that particular combination, storing the set of statistical latent class models in computer hardware; receiving into the recommendation system at least one of: an actual user profile; and a user query; receiving into the recommendation system a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being desired items; when said user query is not received into the recommendation system, generating a user query; prior to the step of computing a probability of relevance, setting preference probabilities below a predetermined threshold to zero, computing a probability of relevance for each item in the set of data utilizing the received set of statistical latent class models and the set of data; and generating at least one recommendation list, each recommendation list having a variable length and consisting of a ranked list of desired items, the items being ranking based on the computed probability of relevance.

Claim 21

A method in a computer system containing a recommendation system for a set of data including items, content descriptors for the items, user profiles about transactions, prior searches, user ratings or user actions, to generate a recommendation list of desired items, comprising the following steps: receiving into the recommendation system a set of statistical latent class models along with appropriate model combination weights, each possible combination of items, content descriptors, users, object attributes and user attributes, and preferences being assigned a probability indicating the likelihood of that particular combination, storing the set of statistical latent class models in computer hardware; receiving into the recommendation system at least one of: an actual user profile; and a user query; receiving into the recommendation system a request to generate at least one recommendation list, items in the recommendation list being ranked by their likelihood of being desired items; when said user query is not received into the recommendation system, generating a user query; prior to the step of computing a probability of relevance, setting preference probabilities below a predetermined threshold to zero, computing a probability of relevance for each item in the set of data utilizing the received set of statistical latent class models and the set of data; and generating at least one recommendation list, each recommendation list having a variable length and consisting of a ranked list of desired items, the items being ranking based on the computed probability of relevance.