Abstract: Methods, systems and computer program products for associating geographical locations with annotations corresponding to content. In one method, a language model is developed. The language model is developed from the location information and the one or more annotations associated with content uploaded by users. The language model is based on the probabilistic distribution of locations over one or more annotations. Further, when a user provides one or more annotations, the system and the method may use the language model to identify one or more locations associated with the one or more annotations provided by the user. The language model predicts one or more geographical locations based on the probabilistic distribution of locations over the annotations.

Abstract: Systems and methods can be implemented to provide suggestions to a user device to execute user tasks in a variety of applications. In various embodiments, a set of tasks of a user from a number of task sources are identified for possible consolidation. The tasks in the set can be analyzed to identify a location to accomplish the tasks, estimate times to complete the tasks at the identified locations, and compute routes to the locations from a starting location or compute routes with respect to a route to another location of another task of the set of tasks. The identified locations, the estimated times, and the computed routes can be used to determine which tasks of the set of tasks are available for consolidation. One or more suggestions of task consolidations can be generated to or within the user device. Additional systems and methods are disclosed.

Abstract: Aspects of the technology described herein are directed towards systems, methods, and computer storage media for, among other things, providing personalized notification management. Notifications can be communicated to a user upon receipt or queued for subsequent handling based on a probability that the user will interact with the notification within a threshold elapsed time from presentation, if it is presented. The probability is determined based on a user's past interactions with similar notifications. The interactions of other users with notifications can also be considered to determine the probability. The notifications can be managed by a notification broker.

Abstract: Systems and methods can be implemented to provide suggestions to a user device to execute user tasks in a variety of applications. In various embodiments, a set of tasks of a user from a number of task sources are identified for possible consolidation. The tasks in the set can be analyzed to identify a location to accomplish the tasks, estimate times to complete the tasks at the identified locations, and compute routes to the locations from a starting location or compute routes with respect to a route to another location of another task of the set of tasks. The identified locations, the estimated times, and the computed routes can be used to determine which tasks of the set of tasks are available for consolidation. One or more suggestions of task consolidations can be generated to or within the user device. Additional systems and methods are disclosed.

Abstract: Aspects of the technology described herein are directed towards systems, methods, and computer storage media for, among other things, providing personalized notification management. Notifications can be communicated to a user upon receipt or queued for subsequent handling based on a probability that the user will interact with the notification within a threshold elapsed time from presentation, if it is presented. The probability is determined based on a user's past interactions with similar notifications. The interactions of other users with notifications can also be considered to determine the probability. The notifications can be managed by a notification broker.

Abstract: Methods, systems and computer program products for associating geographical locations with annotations corresponding to content. In one method, a language model is developed. The language model is developed from the location information and the one or more annotations associated with content uploaded by users. The language model is based on the probabilistic distribution of locations over one or more annotations. Further, when a user provides one or more annotations, the system and the method may use the language model to identify one or more locations associated with the one or more annotations provided by the user. The language model predicts one or more geographical locations based on the probabilistic distribution of locations over the annotations.

Abstract: A technique for training a machine learning model can use time-series data sampled from a population. The training includes creating a training set comprising feature vectors and corresponding labels generated using the time-series data. In some embodiments, for example, the feature vectors can include time-based features generated from the time-series data that preserves time information contained in the time-series data. The labels can be generated using data within a fixed period of time in the time-series data relative to a cut-off date. In some embodiments, the data used to create the training set can use a moving window sampling of the population to account for seasonal effects in the time-series data, where the cut-off date for generating the label varies from one sample to the next.

Abstract: Methods, systems and computer program products for associating geographical locations with annotations corresponding to content. In one method, a language model is developed. The language model is developed from the location information and the one or more annotations associated with content uploaded by users. The language model is based on the probabilistic distribution of locations over one or more annotations. Further, when a user provides one or more annotations, the system and the method may use the language model to identify one or more locations associated with the one or more annotations provided by the user. The language model predicts one or more geographical locations based on the probabilistic distribution of locations over the annotations.

Abstract: Disclosed are methods and apparatus for executing a search query. In accordance with one embodiment, a search query is obtained. The search query is classified into one or more of a plurality of categories. The search query is executed for each of the one or more of the plurality of categories. Search results corresponding to the search query are obtained for each of the one or more of the plurality of categories. The search results are then provided for each of the one or more of the plurality of categories.

Abstract: Methods, systems and computer program products for displaying geographical locations with the one or more annotations. In a particular embodiment, a language model is used to obtain the probability distribution of the locations over one or more annotations. Further, the system and the method utilizes the probability data obtained from the language model to determine a probability score for each location over the one or more annotations. Subsequently, one or more geographical locations are displayed on a world map, based on the probability score of the geographical locations over the one or more annotations. In one embodiment, geographical locations may be highlighted using a color code on a heat map overlaid on the world map. The color code may represent the ranking of the geographical locations based on the calculated probability score for each identified geographical location.

Abstract: Briefly, one or more embodiments of methods, apparatuses or systems for media or content tagging are described.

Abstract: Methods, computer products, and systems for selecting advertisements in response to an internet query are provided. The method provides for receiving an internet query that includes query terms, retrieving and then ranking a first set of advertisements in response to the internet query using a query likelihood model. The method then selects sampling terms using pseudo-relevance feedback and translation models, the internet query, and the first set of ad materials obtained using the query likelihood model. The sampling terms are chosen from a distribution of terms from the terms in the first set of ad materials, and the pseudo-relevance feedback model is used to select a term in the distribution of terms based on a probability. The use of translation models enhances the topicality of the results because the distribution words selected are related to the terms in the original query as indicated by their translation probabilities.

Abstract: Methods, computer products, and systems for selecting advertisements in response to an internet query are provided. The method provides for receiving an internet query having query terms, and retrieving and ranking a first set of advertisements using a query likelihood model. Sampling words are selected using pseudo-relevance feedback and translation models, the internet query, and the first set of ad materials. Sampling words are chosen from the words in the first set, and the pseudo-relevance feedback model is used to select word w in the distribution of words based on a probability that w generates query term q(p(q|w)). The translation model calculates p(q|w) based on a translation probability that w translates into q(t(q|w)). A second set of ad materials are retrieved and ranked using an expanded query which adds the selected sampling words to the original query. The second set of ad materials is presented to the user.

Abstract: Techniques are provided for prediction locations of users that submit search queries. A query is received at a search engine. An inverted index is searched to identify one or more geographical locations associated with one or more terms of the received query. The inverted index lists a plurality of query terms and one or more geographical locations associated with each query term. Each geographic location that is associated with a listed query term in the inverted index is a determined location for at least one user previously having submitted the listed term in a search query. A geographical location is predicted for a user that submitted the received query based on the identified one or more geographical locations. In this manner, a location is predicted for the user based on similar queries previously submitted by users.

Abstract: A user submitting a query from a computer at an unknown location is located using a language model. The language model is derived from an aggregation of tweets that were sent from known locations.

Abstract: A system for and method for ranking results. The system includes a server configured to receive a query and an advertisement engine configured to receive the query from the server. The advertisement engine ranks advertisements based on various features, including at least one word overlap feature and a correlation feature.

Abstract: In one embodiment, a search query is received. Information identifying a bookmark representing the search query is automatically stored in association with a set of bookmarks. Search results corresponding to the search query are automatically obtained and provided, where the search results identify one or more documents. When one of the documents is selected, a link to the selected one of the documents is automatically stored in association with the bookmark.

Abstract: Example methods, apparatuses, or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate or otherwise support one or more processes or operations for identifying points of interest in a text, such as in an unstructured text, for example, in connection with bootstrapping points of interest via social media.

Abstract: A user submitting a query from a computer at an unknown location is located using a language model. The language model is derived from an aggregation of tweets that were sent from known locations.

Abstract: A user submitting a query from a computer at an unknown location is located using a language model. The language model is derived from an aggregation of tweets that were sent from known locations.