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: Methods, systems, and computer programs are presented for selecting content items for presentation to a user device of a user. One method includes an operation for measuring dwelltimes for a plurality of content items, where the dwelltime for a content item is based on the amount of time that the content item is displayed to a viewer. Further, the method includes an operation for training a classifier of content items based on the measured dwelltimes and based on features associated with the first set of content items, where the training includes calculating a relative importance of respective features for increasing measured dwelltimes. Additionally, the method includes an operation for sending additional content items to be presented on the user device based on the one or more ranking parameters, where the additional content items are associated with one or more ranking parameters that are expected to increase dwelltimes for the user of the user device.

Abstract: A system and/or technique for graphical interface presentation comprises receiving a search string and presenting a search result series to a user on a client device. Search results of the search result series are presented in the form of cards, where a first card comprises information associated with a first search result of the search result series and a second card comprises information associated with a second search result of the search result series. A user may navigate through the cards to view various search results.

Abstract: A method, implemented on at least one computing device, each of which has at least one processor, storage, and a communication platform connected to a network for classifying a question is disclosed. A question is received from a person. A question pattern is determined. A model selected based on the question is retrieved. Further, a decision is made as to whether the question is a personal question based on the question pattern and the selected model.

Abstract: Methods, systems, and computer programs are presented for displaying a customized news stream. One method includes an operation for identifying dwell times spent by users while accessing a first plurality of items, each item belonging to one media type from a plurality of media types (e.g., news articles, videos, slide shows, etc.). In addition, the method includes operations for determining statistical parameters for each media type based on the identified dwell times, and for detecting a news corpus having a second plurality of items. A priority for each item in the news corpus is determined based on the media type of the item, the corresponding statistical parameters for the media type of the item, and the profile of a user. The news stream is sent to the user for presentation on a display, the news stream being sorted based on the priority of the items in the news corpus.

Abstract: A method, implemented on at least one computing device, each of which has at least one processor, storage, and a communication platform connected to a network for classifying a question is disclosed. A question is received from a person. A question pattern is determined. A model selected based on the question is retrieved. Further, a decision is made as to whether the question is a personal question based on the question pattern and the selected model.

Abstract: Embodiments of the present teachings disclose method, system, and programs for a multi-phase ranking system for implementation with a personalized content system. The disclosed method, system, and programs utilize a weighted AND system to compute a dot product of the user profile and a content profile in a first phase, a content quality indicator in the second phase and a rules filter in a third phase.

Abstract: Methods, systems, and computer programs are presented for displaying a customized news stream. One method includes an operation for identifying dwell times spent by users while accessing a first plurality of items, each item belonging to one media type from a plurality of media types (e.g., news articles, videos, slide shows, etc.). In addition, the method includes operations for determining statistical parameters for each media type based on the identified dwell times, and for detecting a news corpus having a second plurality of items. A priority for each item in the news corpus is determined based on the media type of the item, the corresponding statistical parameters for the media type of the item, and the profile of a user. The news stream is sent to the user for presentation on a display, the news stream being sorted based on the priority of the items in the news corpus.

Abstract: Methods, systems, and computer programs are presented for selecting content items for presentation to a user device of a user. One method includes an operation for measuring dwelltimes for a plurality of content items, where the dwelltime for a content item is based on the amount of time that the content item is displayed to a viewer. Further, the method includes an operation for training a classifier of content items based on the measured dwelltimes and based on features associated with the first set of content items, where the training includes calculating a relative importance of respective features for increasing measured dwelltimes. Additionally, the method includes an operation for sending additional content items to be presented on the user device based on the one or more ranking parameters, where the additional content items are associated with one or more ranking parameters that are expected to increase dwelltimes for the user of the user device.

Abstract: The present teaching relates to generating a card based on intent. In one example, a request is received for generating a card to be provided to a user. Intent of the user with respect to the card is obtained. One or more modules are selected to be put into the card based on the intent. The card is generated based on the selected one or more modules.

Abstract: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: Methods, systems, and computer programs are presented for selecting news articles for presentation to a user. One method includes an operation for measuring dwelltimes for a first set of news items, where the dwelltime for a news item is based on the amount of time that the news item is displayed to a viewer. Further, the method includes an operation for training a classifier of news items based on the measured dwelltimes and based on features associated with the first set of news items. Additionally, the method includes an operation for ranking with the classifier a second set of news items for presentation to the user, the ranking also using the profile of the user for delivery of customized news to the user. The ranked second set of news item is then presented to the user.

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: A method of targeting receives several granular events and preprocesses the received granular events thereby generating preprocessed data to facilitate construction of a model based on the granular events. The method generates a predictive model by using the preprocessed data. The predictive model is for determining a likelihood of a user action. The method trains the predictive model. A system for targeting includes granular events, a preprocessor for receiving the granular events, a model generator, and a model. The preprocessor has one or more modules for at least one of pruning, aggregation, clustering, and/or filtering. The model generator is for constructing a model based on the granular events, and the model is for determining a likelihood of a user action. The system of some embodiments further includes several users, a selector for selecting a particular set of users from among the several users, a trained model, and a scoring module.

Abstract: A method is provided for building a user interest profile. According to the method, features of each of a plurality of articles are identified. For a given user, views of one or more of the plurality of articles are logged. For each view, a corresponding dwell time for the view by the given user is measured. A weight is applied to each view based on the corresponding measured dwell time. User interest scores for features of the articles are determined based on the weighted views, and the user interest profile is generated for the given user based on the user interest scores.

Abstract: The present teaching relates to online task exchange. In one example, information related to a task automatically created based on an intent of a person is received. The task is posted to a plurality of parties that are capable of completing the task. One or more bids are received from at least some of the plurality of parties. At least one winning bid is selected from the one or more bids based on at least one criterion. Information indicative of a status as to completion of the task is received. Resource is allocated to the at least one winning bid based on the status.

Abstract: The present teaching relates to updating an intent space and estimating intent based on an intent space. In one example, an initial intent space is obtained. Each intent in the initial intent space is characterized in one or more dimensions. At least one model is received. Each of the at least one model provides features in each of the dimensions and relationship thereof. A new intent associated with an intent in the initial intent space is determined based on the at least one model. Based on the new intent, the initial intent space is updated to derive an updated intent space.

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: A method, implemented on at least one computing device each of which has at least one processor, storage, and a communication platform connected to a network for providing synthetic answers to a personal question is disclosed. A personal question is received from a person. One or more entities are extracted from the personal question. One or more relations are extracted from the personal question. A model is selected based on the personal question. One or more synthetic answers to the personal question are obtained based on the one or more entities, the one or more relations, and the selected model.