Abstract: One or more techniques and/or systems for sending push notifications of content items to client devices are provided herein. For example, an input received from a user can be expanded to obtain an expanded user interest. Content items from a content source can be filtered based upon the expanded user interest to obtain a set of filtered content items. A push notification can be constructed to comprise one or more of the filtered content items from the set of filtered content items. The push notification can be sent to a client device of the user for display as a device alert notification. In an example, the filtered content items, within the push notification, may be ranked based upon a ranking metric.

Abstract: One or more techniques and/or systems for sending push notifications of content items to client devices are provided herein. For example, an input received from a user can be expanded to obtain an expanded user interest. Content items from a content source can be filtered based upon the expanded user interest to obtain a set of filtered content items. A push notification can be constructed to comprise one or more of the filtered content items from the set of filtered content items. The push notification can be sent to a client device of the user for display as a device alert notification. In an example, the filtered content items, within the push notification, may be ranked based upon a ranking metric.

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: 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: Techniques are described by which information needs are satisfied by taking into account a variety of contextual cues that constrain the information searched. Contextual data such as the location, speed, and direction of travel of a mobile device are used to generate or modify a search query in a way that constrains the geographic region being searched to improve the relevance of search results or recommendations provided.

Abstract: One or more techniques and/or systems for sending push notifications of content items to client devices are provided herein. For example, an input received from a user can be expanded to obtain an expanded user interest. Content items from a content source can be filtered based upon the expanded user interest to obtain a set of filtered content items. A push notification can be constructed to comprise one or more of the filtered content items from the set of filtered content items. The push notification can be sent to a client device of the user for display as a device alert notification. In an example, the filtered content items, within the push notification, may be ranked based upon a ranking metric.

Abstract: One or more techniques and/or systems for sending push notifications of content items to client devices are provided herein. For example, an input received from a user can be expanded to obtain an expanded user interest. Content items from a content source can be filtered based upon the expanded user interest to obtain a set of filtered content items. A push notification can be constructed to comprise one or more of the filtered content items from the set of filtered content items. The push notification can be sent to a client device of the user for display as a device alert notification. In an example, the filtered content items, within the push notification, may be ranked based upon a ranking metric.

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: The present teaching relates to method, system, and programs for training a ranking model for ranking content items. In one example, a set of content items is obtained. A plurality types of online user activities performed with respect to the set of content items are obtained. For each of the set of content items, a plurality of user engagement scores are determined. Each of the plurality of user engagement scores is determined based on a corresponding one of the plurality types of online user activities. For each of the set of content items, an aggregated score is calculated based on the plurality of user engagement scores to generate aggregated scores. A ranking model is trained based on the aggregated scores.

Abstract: Embodiments of the present teachings disclose method, system, and programs that monetize personalized user behavioral profiles by remapping the users to audience segments related to advertisement. In the method, the users can be targeted with advertisements that are personalized and hence are more likely to lead to conversions.

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 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: 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: Techniques are described by which information needs are satisfied by taking into account a variety of contextual cues that constrain the information searched. Contextual data such as the location, speed, and direction of travel of a mobile device are used to generate or modify a search query in a way that constrains the geographic region being searched to improve the relevance of search results or recommendations provided.

Abstract: A method and apparatus is described for assigning functional labels to segments of web pages in an application-independent way. In the approach described herein, one of a generic set functional labels are automatically assigned to each segment of a web page, where the generic functional labels may be topic-independent and application-independent. Applications with different needs can determine which segments of the web page to process based on which functional labels correspond to the types of information needed by each application. Thus, the work of classifying the function of each segment of a web page is separated from the work of selecting which segments satisfy the need of a particular application. The work of classification can be performed in an application-independent way, relieving the burden from every application developer from having to create their own classifiers.

Abstract: One or more techniques and/or systems for sending push notifications of content items to client devices are provided herein. For example, an input received from a user can be expanded to obtain an expanded user interest. Content items from a content source can be filtered based upon the expanded user interest to obtain a set of filtered content items. A push notification can be constructed to comprise one or more of the filtered content items from the set of filtered content items. The push notification can be sent to a client device of the user for display as a device alert notification. In an example, the filtered content items, within the push notification, may be ranked based upon a ranking metric.

Abstract: Methods, systems and programming for targeting users with engaging content. In one example, a metric with respect to a piece of content is measured for each of a plurality of users. A first set of users is identified from the plurality of users based on the measured metrics and a threshold. User profiles of the first set of users are obtained. A second set of users is then identified based on the user profiles of the first set of users. The piece of content is provided to the second set of users.

Abstract: Embodiments of the present teachings disclose method, system, and programs that monetize personalized user behavioral profiles by remapping the users to audience segments related to advertisement.