Abstract: A method for determining user interests is provided, the method comprising: storing data items relating to usage patterns of the user, wherein the data items include an interest portion and a context portion; grouping the data items into context groups, each context group having data items with related context portions; for each context group, determining if the number of data items in the context group is greater than or equal to a first threshold; creating a first partition having context groups having a number of data items greater than or equal to the first threshold; averaging the ratings for interests in the data items in the context groups in the first partition, resulting in each context group in the first partition being a cluster; and deriving a user's interest by comparing a current context to a context group in the first partition.

Abstract: Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.

Abstract: Access permission can be assigned to a particular individually executable portion of computer executable code (“component-specific access permission”) and enforced in connection with accessing the services of a service provider by the individually executable portion (or component). It should be noted that least one of the individually executable portions can request the services when executed by a dynamically scalable computing resource provider. In addition, general and component-specific access permissions respectively associated with executable computer code as a whole or one of it specific portions (or components) can be cancelled or rendered inoperable in response to an explicit request for cancellation.

Abstract: Given a situation, an interest in a first object of interest can be determined. In the given situation, interest in a first object of interest is initially unknown and interest in a second object of interest is known. Data is obtained. The obtained data can, for example, include documents from the Internet or other forms of information from a network and/or database. The number of joint occurrences of the first object of interest and the second object of interest in the data is determined. Based on this number, at least one correlation value is determined. Based on the at least one correlation value, an interest value is determined. The interest value indicates the interest in the first object of interest in the given situation.

Abstract: Techniques for determining an interest in an object of interest in a given situation are disclosed. Multiple situation-based interest rating components can be provided for various situations. Each situation-based interest rating component can include an interest value indicative of interest in an object of interest in one of the situations. An input situation can then be obtained. One of the situation-based interest rating components can be identified matching an input situation. The relevance of one or more of the other situation-based interest rating components to the identified matching component can then be determined. This can, for example, be done by computing one or more distances between only the respective situation-based portions of the matching situation-based interest rating component and one or more of the other components, or based on the interest value-based portion of each component, or both.

Abstract: An interest in an object of interest in a given situation can be determined by determining multiple sets of probability values for specific interest levels. Each set of probability values includes a probability that a specific interest level occurs in a situation represented by a plurality of context variables each having a plurality of possible context values. Input context values are then obtained. The relevance of each one of the sets of probability values to the input context values can be determined in order to determine a projected interest level.

Abstract: An input situation can be represented by at least a first context variable. Data that includes interest values for multiple context variables can be provided and obtained. The obtained data can include a first data pertaining to the input situation and a second data pertaining to one or more other situations. It can be determined whether the first context variable is associated with a discrete range of values or a continuous range of values. At least a portion of data pertaining to the situations can be determined to be proximate data when the first context variable is associated with a continuous range of values. Based on the input situation and the proximate data, an interest value for the first input situation can be determined as a prediction of the interest in the input situation.

Abstract: Techniques for assessing the cost of allocation of execution and affecting the allocation of execution are disclosed. The cost of allocation of execution to or between a first computing device (e.g., a mobile device) and one or more computing resource providers (e.g., one or more Clouds) can be determined during runtime of the executable code. It will be appreciated that a computing system can operate independently of the first computing device and one or more computing resource providers and provide execution allocation cost assessment as a service to the first computing device and/or one or more computing resource providers. Execution allocation cost can be assessed (or determined) based on execution allocation data pertaining to the first computing device and/or one or more computing resource providers. By way of example, power consumption of a mobile device can be used as a factor in determining how to allocate individual components of an application program (e.g.

Abstract: A method for generating a portion of a query for a user is provided, the method comprising: retrieving information regarding the user's interests; and generating a portion of a query by analyzing the user's interests and the current situation of a user device operated by the user to determine at least one keyword.

Abstract: In a first embodiment of the present invention, a method for automated creation of a mashup is provided, the method comprising: receiving data needs of a user; identifying sources of data to satisfy the data needs by comparing the data needs to available data sources; retrieving metadata relating to the identified sources of data from a source metadata store; identifying services to satisfy the data needs by comparing the retrieved metadata to available services; retrieving metadata related to the identified services from a service metadata store; and generating a plan for supplying data from the identified sources of data to the identified services based on the retrieved metadata from the source metadata source and the retrieved metadata from the service metadata source.

Abstract: In one embodiment, a method for operating a local virtual world proxy is provided. A virtual world view is received corresponding to a virtual world client. Information about resources available to the virtual world client is obtained. Virtual representations of one or more resources available to a virtual world client are fetched. Then the virtual representations are injected into the virtual world view. The virtual world view is then forwarded to the virtual world client for display to a user of the virtual world client.

Abstract: Data relating to usage patterns of the user are stored. The data includes a context portion having information as to the context in which items were used and an interest rating portion indicative of an interest of the user in one or more objects of interest. The data is clustered into clusters of data points. For each of the clusters, a centroid is determined. The centroid includes a context portion and an interest rating portion. A current context of the user is received. Clusters are selected by comparing a data point representing the current context with the context portion of one or more centroids. Based on the selected clusters, an interest rating is computed. The computed interest rating indicates an interest of the user in one of the one or more objects of interest, given the current context.

Abstract: Data relating to usage patterns of the user is stored, wherein the data includes an application portion having information as to items which were used and a context portion having information as to the context in which the items were used. The data is clustered into clusters of data points and centroid are computed, wherein the centroid includes an application portion and a context portion. Clusters similar to a current context of the user are selected by comparing a data point representing the current context of the user to the context portions of one or more of the centroids. For each of one or more items, a expectation value that the user wishes to use the corresponding item is computed, based on the application portions of the selected similar clusters, wherein the expectation values are used to recommend one or more of the items.

Abstract: A definition of a set of context variables to be considered is received, wherein the context variables represent categories of situations in which the computer system can be. A definition of a set of activities to be considered is also received, wherein the activities represent activities that can be performed using the computer system. For each context variable to be considered, a definition of a set of states to be considered is received, wherein the states represent situations in which the computer system can be. For each context variable to be considered, a specification of first probability distributions for each corresponding state is received. For each context variable to be considered, for each activity to be considered, a specification of second probability distributions to represent the likelihood of the activity being performed in the corresponding context is received. Usage data is generated using the first second probability distributions.

Abstract: In one embodiment, data relating to usage patterns of the user is stored, wherein the data includes information as to items which were used and the context in which they were used. The data is then clustered into clusters of data points. Then a centroid is determined for each of the clusters. A cluster similar to a current context of the user is selected by comparing a data point representing the current context of the user to one or more of the centroids. For each of one or more items, a threshold based on values for a plurality of the centroids with respect to the corresponding item, wherein a threshold is used to compare with centroid value of an item in a selected cluster to determine whether to recommend the item.

Abstract: In one embodiment, a method for social networking is provided. A social profile is automatically built for a first user by monitoring the usage of an electronic device operated by the first user. A social network appropriate for the first user is determined by examining the first user's social profile. The appropriate social network is provided to the first user to join if the appropriate social network is available for the first user to join.

Abstract: A method for determining user interests is provided, the method comprising: storing data items relating to usage patterns of the user, wherein the data items include an interest portion and a context portion; grouping the data items into context groups, each context group having data items with related context portions; for each context group, determining if the number of data items in the context group is greater than or equal to a first threshold; creating a first partition having context groups having a number of data items greater than or equal to the first threshold; averaging the ratings for interests in the data items in the context groups in the first partition, resulting in each context group in the first partition being a cluster; and deriving a user's interest by comparing a current context to a context group in the first partition.

Abstract: In one embodiment, data relating to usage patterns of the user is stored, wherein the data includes information as to items which were used and the context in which they were used. The data is then clustered into input clusters of data points. It is determined if there are any input clusters that are similar to each other. Similar clusters are merged if there are any input clusters similar to each other. Any non-merged input clusters are divided into split clusters if the split clusters would not be similar to each other. The determining, merging, and dividing are then repeated using the merged, divided, and remaining unmerged and undivided clusters as input clusters.

Abstract: In one embodiment, data relating to usage patterns of the user is stored, wherein the data includes information as to items which were used and the context in which they were used. A correlation table is constructed. Correlation values between each item and each context are then stored in then correlation table, wherein the correlations are used to recommend one or more of the items.

Abstract: Techniques for determining situational patterns of use of computing systems are disclosed. A situational pattern of use can be determined based on the situations encountered by the computing system as the situations occur without having to predefine a set of situations. Generally, a situation can be determined and/or defined based on the context of use of the computing system when the use occurs. The context of use can, for example, be determined based on internal and external variables including the physical environment where a device is used and biological data associated with a person who uses the device. The state of use of the computing system can, for example, be determined based on the state (or status) of one or more components of the computing system (e.g., the state of one or more active applications that are being used by person). Similar to the context of use, the state of use can be determined as the use occurs without having to predefine potential uses of the computing system (e.g.