Abstract: Systems and methods for recommending items to users include maintaining a database of user-related information including user profiles, each including at least a history of user activities associated with a first entity that has a relationship with an inventory of recommendable items; obtaining information about an identity of a user interacting with a second entity different from the first entity and different from the service provider; associating the identity of the user interacting with the second entity with a corresponding user profile; selecting a first set of items from the inventory for presentation to the user based at least on an analysis of a history of user activities associated with the first entity; and forming a specification of the selected first set items for presentation to the user during the user's interaction with the second entity.

Abstract: A method for recommending items in a domain to users, either individually or in groups, makes user of users' characteristics, their carefully elicited preferences, and a history of their ratings of the items are maintained in a database. Users are assigned to cohorts that are constructed such that significant between-cohort differences emerge in the distribution of preferences. Cohort-specific parameters and their precisions are computed using the database, which enable calculation of a risk-adjusted rating for any of the items by a typical non-specific user belonging to the cohort. Personalized modifications of the cohort parameters for individual users are computed using the individual-specific history of ratings and stated preferences. These personalized parameters enable calculation of a individual-specific risk-adjusted rating of any of the items relevant to the user. The method is also applicable to recommending items suitable to groups of joint users such a group of friends or a family.

Abstract: Techniques for gathering and tracking build information are described. Builds are registered initially. When a test execution is performed, a matching build is automatically determined from those previously registered builds as matching the software under test for a particular session. This information is tabulated in a database in accordance with additional software and hardware or platform information describes the environment in which a program executes. The platform information includes software component information and system configuration information uniquely identifying a particular instance of an environment. Data stored in the database is then analyzed and available for performing queries, for example, such as regarding code volatility and determining testing associated with a particular build.

Abstract: Techniques for gathering and tracking build information are described. Builds are registered initially. When a test execution is performed, a matching build is automatically determined from those previously registered builds as matching the software under test for a particular session. This information is tabulated in a database in accordance with additional software and hardware or platform information describes the environment in which a program executes. The platform information includes software component information and system configuration information uniquely identifying a particular instance of an environment. Data stored in the database is then analyzed and available for performing queries, for example, such as regarding code volatility and determining testing associated with a particular build.

Abstract: A method for recommending items in a domain to users, either individually or in groups, makes user of users' characteristics, their carefully elicited preferences, and a history of their ratings of the items are maintained in a database. Users are assigned to cohorts that are constructed such that significant between-cohort differences emerge in the distribution of preferences. Cohort-specific parameters and their precisions are computed using the database, which enable calculation of a risk-adjusted rating for any of the items by a typical non-specific user belonging to the cohort. Personalized modifications of the cohort parameters for individual users are computed using the individual-specific history of ratings and stated preferences. These personalized parameters enable calculation of a individual-specific risk-adjusted rating of any of the items relevant to the user. The method is also applicable to recommending items suitable to groups of joint users such a group of friends or a family.

Abstract: A method for recommending items in a domain to users, either individually or in groups, makes user of users' characteristics, their carefully elicited preferences, and a history of their ratings of the items are maintained in a database. Users are assigned to cohorts that are constructed such that significant between-cohort differences emerge in the distribution of preferences. Cohort-specific parameters and their precisions are computed using the database, which enable calculation of a risk-adjusted rating for any of the items by a typical non-specific user belonging to the cohort. Personalized modifications of the cohort parameters for individual users are computed using the individual-specific history of ratings and stated preferences. These personalized parameters enable calculation of a individual-specific risk-adjusted rating of any of the items relevant to the user. The method is also applicable to recommending items suitable to groups of joint users such a group of friends or a family.