Abstract: Systems and methods have been developed to increase user compliance and adherence to respiratory therapy devices. For instance, in some examples the disclosed technology may monitor usage data output from a respiratory therapy device to determine, based on the trends of usage, when a user is likely to terminate or reduce usage within a specified time window. Flagging a user may also trigger further actions to automatically intervene before the user terminates engagement with the service.

Abstract: Method, system, and programs for measuring user engagement. In one example, a model generated based on user activities with respect to a plurality pieces of content is obtained. One or more actual occurrences of the user activities with respect to one piece of the plurality pieces of content are identified. One or more future occurrences of the user activities with respect to the piece of content are estimated based on the model. A user engagement score with respect to the piece of content is calculated based on the one or more actual occurrences of the user activities and the one or more future occurrences of the user activities.

Abstract: Systems and methods have been developed to increase user compliance and adherence to various devices and services including respiratory therapy devices, exercise equipment, and online or other software services. For instance, in some examples the disclosed technology may monitor usage data output from a respiratory therapy device, exercise equipment or computer software program to determine, based on the trends of usage, when a user is likely to terminate or reduce usage within a specified time window. Flagging a user may also trigger further actions to automatically intervene before the user terminates engagement with the service.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing content from multiple disparate sources including a person's personal data sources and non-personal data sources. The disclosed techniques may include receiving a request for content from a person; obtaining first content from a first source private to the person based on the request; obtaining second content from at least one second source based on the request; blending the first content from the first source and the second content from the at least one second source to generate a blended content; and providing the blended content to the person in response to the request.

Abstract: Method, system, and programs for measuring user engagement. In one example, a model generated based on user activities with respect to a plurality pieces of content is obtained. One or more actual occurrences of the user activities with respect to one piece of the plurality pieces of content are identified. One or more future occurrences of the user activities with respect to the piece of content are estimated based on the model. A user engagement score with respect to the piece of content is calculated based on the one or more actual occurrences of the user activities and the one or more future occurrences of the user activities.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to ranking content from multiple disparate sources including a person's personal data sources and non-personal data sources. The disclosed techniques may include obtaining a plurality sets of content associated with a request from a person, each of which being from a separate data source, and applying a model for each set of content to obtain a set of features for each piece of content in the set of content, wherein the model is specific to a data source from where the set of content comes from. Each set of features for each piece of content of the set of content may be normalized with respect to a common space to generate a normalized feature set. Further, a score for each piece of content from a set of content may be estimated based on the normalized feature set for the piece of content, and based on the score of the piece of content, each piece of content of the plurality sets of content may be ranked.

Abstract: Briefly, embodiments of methods and/or systems for performing content recommendation are disclosed. For one embodiment, as an example, estimating relevance may include computing an inner product of latent factors corresponding to a plurality of users and features of one or more content items.

Abstract: Method, system, and programs for measuring user engagement. In one example, a model generated based on user activities with respect to a plurality pieces of content is obtained. One or more actual occurrences of the user activities with respect to one piece of the plurality pieces of content are identified. One or more future occurrences of the user activities with respect to the piece of content are estimated based on the model. A user engagement score with respect to the piece of content is calculated based on the one or more actual occurrences of the user activities and the one or more future occurrences of the user activities.

Abstract: Method, system, and programs for measuring user engagement. In one example, a model generated based on user activities with respect to a plurality pieces of content is obtained. One or more actual occurrences of the user activities with respect to one piece of the plurality pieces of content are identified. One or more future occurrences of the user activities with respect to the piece of content are estimated based on the model. A user engagement score with respect to the piece of content is calculated based on the one or more actual occurrences of the user activities and the one or more future occurrences of the user activities.

Abstract: Method, system, and programs for measuring user engagement. In one example, a model generated based on user activities with respect to a plurality pieces of content is obtained. One or more actual occurrences of the user activities with respect to one piece of the plurality pieces of content are identified. One or more future occurrences of the user activities with respect to the piece of content are estimated based on the model. A user engagement score with respect to the piece of content is calculated based on the one or more actual occurrences of the user activities and the one or more future occurrences of the user activities.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to ranking content from multiple disparate sources including a person's personal data sources and non-personal data sources. The disclosed techniques may include obtaining a plurality sets of content associated with a request from a person, each of which being from a separate data source, and applying a model for each set of content to obtain a set of features for each piece of content in the set of content, wherein the model is specific to a data source from where the set of content comes from. Each set of features for each piece of content of the set of content may be normalized with respect to a common space to generate a normalized feature set. Further, a score for each piece of content from a set of content may be estimated based on the normalized feature set for the piece of content, and based on the score of the piece of content, each piece of content of the plurality sets of content may be ranked.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to ranking content from multiple disparate sources including a person's personal data sources and non-personal data sources. The disclosed techniques may include obtaining a plurality sets of content associated with a request from a person, each of which being from a separate data source, and applying a model for each set of content to obtain a set of features for each piece of content in the set of content, wherein the model is specific to a data source from where the set of content comes from. Each set of features for each piece of content of the set of content may be normalized with respect to a common space to generate a normalized feature set. Further, a score for each piece of content from a set of content may be estimated based on the normalized feature set for the piece of content, and based on the score of the piece of content, each piece of content of the plurality sets of content may be ranked.

Abstract: Briefly, embodiments of methods and/or systems for performing content recommendation are disclosed. For one embodiment, as an example, estimating relevance may include computing an inner product of latent factors corresponding to a plurality of users and features of one or more content items.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to ranking content from multiple disparate sources including a person's personal data sources and non-personal data sources. The disclosed techniques may include obtaining a plurality sets of content associated with a request from a person, each of which being from a separate data source, and applying a model for each set of content to obtain a set of features for each piece of content in the set of content, wherein the model is specific to a data source from where the set of content comes from. Each set of features for each piece of content of the set of content may be normalized with respect to a common space to generate a normalized feature set. Further, a score for each piece of content from a set of content may be estimated based on the normalized feature set for the piece of content, and based on the score of the piece of content, each piece of content of the plurality sets of content may be ranked.

Abstract: The present teaching, which includes methods, systems and computer-readable media, relates to providing content from multiple disparate sources including a person's personal data sources and non-personal data sources. The disclosed techniques may include receiving a request for content from a person; obtaining first content from a first source private to the person based on the request; obtaining second content from at least one second source based on the request; blending the first content from the first source and the second content from the at least one second source to generate a blended content; and providing the blended content to the person in response to the request.

Abstract: Method, system, and programs for mapping data. Information related to users and their interests is obtained by a first application from a second application. An interest space is determined based on one or more sources of information, each of which provides a plurality of concepts. A data structure is created with respect to the interest space, where the data structure has a plurality of attributes each of the attributes corresponds to a concept in the interest space. One or more interests for each of the users based on information obtained from the second application. Each user interest corresponds to an attribute in the structure. A user profile is generated for each user by mapping the interests of the user to the corresponding attributes in the structure.

Abstract: The present teaching relates to discovery of user unknown interests. In one example, information related to a user is retrieved from a user profile. The information indicates one or more known interests of the user. At least one known interest of the user is identified based on the information. One or more supplemental interests with respect to each identified at least one known interest of the user are identified. The one or more supplemental interests do not overlap with the one or more known interests of the user. Supplemental content associated with the one or more supplemental interests are identified. Each piece of content in the supplemental content is ranked. At least one piece of content in the supplemental content is selected based on the ranking. The selected at least one piece of supplemental content is used to discover unknown interest of the user.

Abstract: Method, system, and programs for mapping data. Information related to users and their interests is obtained by a first application from a second application. An interest space is determined based on one or more sources of information, each of which provides a plurality of concepts. A data structure is created with respect to the interest space, where the data structure has a plurality of attributes each of the attributes corresponds to a concept in the interest space. One or more interests for each of the users based on information obtained from the second application. Each user interest corresponds to an attribute in the structure. A user profile is generated for each user by mapping the interests of the user to the corresponding attributes in the structure.

Abstract: A method and system for exploring a list of user interests beyond the currently known user interests by defining a distance metrics in the interest space is disclosed. The new method and system target for exploration, items of interests which are close in proximity to the current set of user interests, thereby greatly improving the chance that one of the exploration items will be liked by the user.

Abstract: Method, system, and programs for measuring user engagement. In one example, a model generated based on user activities with respect to a plurality pieces of content is obtained. One or more actual occurrences of the user activities with respect to one piece of the plurality pieces of content are identified. One or more future occurrences of the user activities with respect to the piece of content are estimated based on the model. A user engagement score with respect to the piece of content is calculated based on the one or more actual occurrences of the user activities and the one or more future occurrences of the user activities.