Abstract: User-preference datapoints are collected. At least some of these datapoints are associated with user-preference information about an item (e.g., a movie), and some of these datapoints are associated with user-preference information about an attribute (e.g., a movie genre or an actor). A profile of the user is created based, at least in part, on these datapoints. When a new datapoint is collected, the new datapoint is assigned a user-preference “score.” If, for example, the new datapoint does not come with an explicit user-preference rating, then the score is based on related item and attribute datapoints already in the profile. Depending upon the relationship of the new datapoint to the already existing datapoints, a confidence value is assigned to the user-preference score. The profile is then updated with the new datapoint along with its assigned score and confidence rating. The user profile can be used in performing any number of actions.

Abstract: In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

Abstract: In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

Abstract: Disclosed are methods and apparatus for processing linguistic expressions (e.g., opinionated text documents). The linguistic expressions are processed by, firstly, detecting topics of interest discussed in the linguistic expressions. The sentiment, or sentiments, of an originator with respect to each of the topics detected in the linguistic expressions is then assessed. The originators are then grouped (or clustered) into one or more groups based on the similarities between the originators' respective sets of detected topics and corresponding sentiments. Semantic information is then associated with a given group. Finally, for a given member of a given group, a profile is created or updated. This profile comprises attributes that may be based on a degree of membership of the given member to the given group and the semantic information associated with the given group.

Abstract: User-preference datapoints are collected. At least some of these datapoints are associated with user-preference information about an item (e.g., a movie), and some of these datapoints are associated with user-preference information about an attribute (e.g., a movie genre or an actor). A profile of the user is created based, at least in part, on these datapoints. When a new datapoint is collected, the new datapoint is assigned a user-preference “score.” If, for example, the new datapoint does not come with an explicit user-preference rating, then the score is based on related item and attribute datapoints already in the profile. Depending upon the relationship of the new datapoint to the already existing datapoints, a confidence value is assigned to the user-preference score. The profile is then updated with the new datapoint along with its assigned score and confidence rating. The user profile can be used in performing any number of actions.

Abstract: Disclosed are a variety of methods and systems for processing access-only user-behavior data and developing and using user-preference models. In one example embodiment, a method for ascribing a score to a first portion of preference data includes establishing a model of user-preference data and receiving the first portion of preference data at a first computerized device and storing that data. The method further includes calculating at least one statistic in relation to the first portion of the preference data by way of a processing device of either the first computerized device or a second computerized device and performing at least one additional operation, by way of either the processing device or another processing device, by which the at least one statistic is evaluated in relation to the model, whereby as a result of being evaluated, the at least one statistic is converted into the score.

Abstract: Disclosed are methods and apparatus for processing linguistic expressions (e.g., opinionated text documents). The linguistic expressions are processed by, firstly, detecting topics of interest discussed in the linguistic expressions. The sentiment, or sentiments, of an originator with respect to each of the topics detected in the linguistic expressions is then assessed. The originators are then grouped (or clustered) into one or more groups based on the similarities between the originators' respective sets of detected topics and corresponding sentiments. Semantic information is then associated with a given group. Finally, for a given member of a given group, a profile is created or updated. This profile comprises attributes that may be based on a degree of membership of the given member to the given group and the semantic information associated with the given group.

Abstract: In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

Abstract: In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

Abstract: In a hierarchical profile, each node represents at least one feature of behavioral data collected about an entity profiled, with the topmost node selected as the “statistically most informative” feature of the data. A profile can cover numerous domains and be predictively very powerful in each domain. A number of observations can be “aggregated” together into a single datapoint. In use, the structure of the profile is compared against current information associated with the entity to produce a recommendation or prediction. If the profile represents at least some data aggregation, then new observations are folded into the profile based on statistical weights of the aggregations. Because of the way the profile is created and updated, its hierarchical structure maps the collected observations. Therefore, as new observations are incorporated, if the new observations change the profile's structure significantly, then it can be hypothesized that something “interesting” has happened to the entity.

Abstract: A system and methods of managing privacy settings of a user are presented here. The system obtains context information that is indicative of a contextual scenario associated with operation of a user device and determines, with a first analytics system, a first set of privacy settings predictions that is influenced at least in part by the context information. A second analytics system is used to determine a second set of privacy settings predictions that is influenced at least in part by the context information. When the first set of privacy settings predictions differ from the second set of privacy settings predictions by at least a threshold amount, the system issues a query for user-specified privacy settings for the contextual scenario.