Abstract: Techniques train a tree to identify offers to send to a particular customer. Messages that include offers and having attributes are sent to a target user group. Feature measure results from the messages on the target user group, is used with feature measure results for a control user group, to train the tree with branch splits being identified based on maximizing an information gain from the feature measure results for a message/user attribute, where each node within the tree includes target and control distributions for the feature measure. The tree is traversed for a given marketing message/user, drawing randomly from feature measure distributions in the tree to determine whether to send the given marketing message to the user. By drawing randomly from the feature measure distributions, exploration and exploitation of various messages may be performed to minimize ignoring of messages that may have an information gain for particular customers.

Abstract: Techniques are described for determining locations of interest based on user visits. In some situations, the techniques include obtaining information about actual locations of users at various times, and automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Abstract: Techniques are described for determining locations of interest based on user visits. In some situations, the techniques include obtaining information about actual locations of users at various times, and automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Abstract: Techniques are described for determining and using relevant information related to domains of interest. In at least some situations, the techniques include automatically analyzing documents, terms and other information related to a domain of interest in order to automatically determine information about relevant themes within the domain and/or about which documents have contents that are relevant to such themes. Such automatically determined information related to a domain may then be used in various ways, including to assist users in specifying themes of interest and/or in obtaining documents and/or document fragments with contents that are relevant to specified themes. In addition, information about how the automatically determined information is used by users may be tracked and used as feedback for learning improved determinations of relevant themes and relevant documents within the domain, such as by using automated machine learning techniques.

Abstract: Techniques are described for automatically analyzing historical information about road traffic flow in order to generate representative information regarding current or future road traffic flow, and for using such generated representative traffic flow information. Representative traffic flow information may be generated for a variety of types of useful measures of traffic flow, such as for average speed at each of multiple road locations during each of multiple time periods. Generated representative traffic flow information may be used in various ways to assist in travel and for other purposes, such as to determine likely travel times and plan optimal routes. The historical traffic data used to generate the representative traffic flow information may include data readings from physical sensors that are near or embedded in the roads, and/or data samples from vehicles and other mobile data sources traveling on the roads.

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Abstract: Techniques are described for displaying or otherwise providing information to users regarding various types of road traffic condition information in various ways. The information may be provided, for example, as part of a user interface (or “UI”), which may in some situations further include one or more types of user-selectable controls to allow a user to manipulate in various ways what road traffic condition information is displayed and/or how the information is displayed. A variety of types of road traffic condition information may be presented to users in various manners, including by presenting information on graphically displayed maps for geographic areas to indicate various information about road conditions in the geographic area. In addition, provided controls may allow users to select particular times, select particular routes, indicate to perform animation of various types of changing traffic conditions over a sequence of multiple successive times, etc.

Abstract: Techniques are described for determining and using information related to domains of interest, such as by automatically analyzing documents and other information related to a domain in order to automatically determine relationships between particular terms within the domain. Such automatically determined information may then be used to assist users in obtaining information from the domain that is of interest (e.g., documents with contents that are relevant to user-specified terms and/or to other terms that are determined to be sufficiently related to the user-specified terms). For example, recommendations may be automatically generated for a user by using information about specified preferences or other interests of the user with respect to one or more terms and identifying other particular terms that are sufficiently probable to be of interest to that user, such as based on a generated probabilistic representation of relationships between particular terms for the domain.

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Abstract: Techniques are described for determining and using information related to domains of interest, such as by automatically analyzing documents and other information related to a domain in order to automatically determine relationships between particular terms within the domain. Such automatically determined information may then be used to assist users in obtaining information from the domain that is of interest (e.g., documents with contents that are relevant to user-specified terms and/or to other terms that are determined to be sufficiently related to the user-specified terms). For example, recommendations may be automatically generated for a user by using information about specified preferences or other interests of the user with respect to one or more terms and identifying other particular terms that are sufficiently probable to be of interest to that user, such as based on a generated probabilistic representation of relationships between particular terms for the domain.

Abstract: Techniques are described for determining and using relevant information related to domains of interest. In at least some situations, the techniques include automatically analyzing documents, terms and other information related to a domain of interest in order to automatically determine information about relevant themes within the domain and/or about which documents have contents that are relevant to such themes. Such automatically determined information related to a domain may then be used in various ways, including to assist users in specifying themes of interest and/or in obtaining documents and/or document fragments with contents that are relevant to specified themes. In addition, information about how the automatically determined information is used by users may be tracked and used as feedback for learning improved determinations of relevant themes and relevant documents within the domain, such as by using automated machine learning techniques.

Abstract: Techniques are described for determining locations of interest based on user visits. In some situations, the techniques include obtaining information about actual locations of users at various times, and automatically analyzing the information to determine particular locations in a geographic area that are of interest, such as for frequent destinations visited by users. After determining a particular location of interest, it may be represented by generating a corresponding location model to describe the geographic subarea or other location point(s) covered by the determined location of interest, and one or more points of interest (e.g., businesses, parks, schools, landmarks, etc.) may be identified that are located at or otherwise correspond to the determined location of interest. In addition, a determined location of interest may be further used in various ways, including to identify later user visits to that location (e.g., to a point of interest identified for the location).

Abstract: Techniques are described for generating predictions of future traffic conditions at multiple future times, such as by using probabilistic techniques to assess various input data while repeatedly producing future time series predictions for each of numerous road segments (e.g., in a real-time manner based on changing current conditions for a network of roads in a given geographic area). In some situations, one or more predictive Bayesian models and corresponding decision trees are automatically created for use in generating the future traffic condition predictions for each geographic area of interest, such as based on observed historical traffic conditions for those geographic areas. Predicted future traffic condition information may then be used in a variety of ways to assist in travel and for other purposes, such as to plan optimal routes through a network of roads based on predictions about traffic conditions for the roads at multiple future times.

Abstract: Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).