Abstract: The present application describes various methods and devices for providing content to users. In one aspect, a method includes, for each content item of a set of content items, obtaining a score for the content item using a recommender system, the score corresponding to a calculation of subsequent repeated engagement by a user with the content item. The method also includes ranking the set of content items based on the respective scores and providing recommendation information to the user for one or more highest ranked content items in the set of content items.

Abstract: Disclosed are devices, systems, apparatus, methods, products, media, and other implementations, including a method that includes generating for a code segment of a first process an instruction dependency graph representative of behavior of the first process, obtaining respective one or more instruction dependency graphs representative of behaviors of code segments for one or more other processes, and determining, based on the first instruction dependency graph for the first process and the respective one or more instruction dependency graphs for the one or more other processes, a level of similarity between the first process and at least one of the one or more other processes.

Abstract: A system for executing composite tasks can include a processor to detect a composite task from a user. The processor can also detect a plurality of subtasks corresponding to the composite task based on unsupervised data without a label, wherein the plurality of subtasks are identified by a top-level dialog policy. The processor can also detect a plurality of actions, wherein each action is to complete one of the subtasks, and wherein each action is identified by a low-level dialog policy corresponding to the subtasks identified by the top-level dialog policy. The processor can also update a dialog manager based on a completion of each action corresponding to the subtasks and execute instructions based on a policy identified by the dialog manager, wherein the executed instructions implement the policy with a lowest global cost corresponding to the composite task provided by the user.

Abstract: A system for reducing the information content of a data stream according to privacy requirements that vary according to referents of the data while maximizing the utility of the data stream in the aggregate. In embodiments, a receiver of data characterizing multiple referents extracts information such as statistics. A filter may reduce the information content of the data to reduce the probability that the receiver could uniquely identify any single referent from the data, according to privacy requirements that vary by the referent. The filter allows this to be done in a way that allows the utility of the data to be maximized when the permitted probability of identification varies among the referents.

Abstract: Disclosed are devices, systems, apparatus, methods, products, media, and other implementations, including a method that includes generating for a code segment of a first process an instruction dependency graph representative of behavior of the first process, obtaining respective one or more instruction dependency graphs representative of behaviors of code segments for one or more other processes, and determining, based on the first instruction dependency graph for the first process and the respective one or more instruction dependency graphs for the one or more other processes, a level of similarity between the first process and at least one of the one or more other processes.

Abstract: A system, apparatus, method, and computer-readable medium for optimizing classifiers are disclosed. The optimization process can include receiving one or more training examples. The optimization process can further include assigning a loss parameter to each training example. The optimization process can further include optimizing each loss parameter of each training sample based on a sample variance of each training example using a non-linear function. The optimization process can further include estimating a classifier from the one or more weighted training samples. The optimization process can further include assigning a loss parameter to the classifier based on a number of training examples that the classifier correctly classified and a number of training examples that the classifier incorrectly classified. The optimization process can further include adding the weighted classifier to an overall classifier.

Abstract: A system for reducing the information content of a data stream according to privacy requirements that vary according to referents of the data while maximizing the utility of the data stream in the aggregate. In embodiments, a receiver of data characterizing multiple referents extracts information such as statistics. A filter may reduce the information content of the data to reduce the probability that the receiver could uniquely identify any single referent from the data, according to privacy requirements that vary by the referent. The filter allows this to be done in a way that allows the utility of the data to be maximized when the permitted probability of identification varies among the referents.

Abstract: A method, system, computer program product and computer readable media for matching using degree distribution information are disclosed. An embodiment of the method can include performing b-matching on a graph data structure expanded using degree distribution information in order to identify neighbors of a selected input node. The b-matching can be performed using belief propagation. The belief propagation method is adapted to use a compressed message update rule and to be suitable for use with distributed processing systems. An embodiment can also include enhancing a matching result by applying degree distribution information to a first matching result to generate a second matching result. Embodiments for online advertisement/search term matching, product recommendation, dating service and social network matching, auction buyer/seller matching and resource allocation, among other, are disclosed.

Abstract: Methods and systems for predicting links in a network, such as a social network, are disclosed. The existing network structure can be used to optimize link prediction. The methods and systems can learn a distance metric and/or a degree preference function that are structure preserving to predict links for new/existing nodes based on node properties.

Abstract: Entities may be matched to enhance the efficiency of various commercial activities using various system and method embodiments of the disclosed subject matter. Belief propagation on a graph data structure defining a bipartite or unipartite matching opportunity is used to calculate a best matching. In embodiments, functions are implemented based upon the match, such as executing sales between matched buyers and sellers in an online auction system. In embodiments, messages with scalar values carry information about the relative value of possible matchings, initially provided as weights or values for the possible matchings. Weights may depend on, for example, bids or costs. Messages may be passed, for example over a network between processors respective to the nodes. Belief values reflecting a best matching can be continuously updated for each node responsively to the value information and received messages to rank the matches respective to each node, which progressively improve.

Abstract: Methods and systems for predicting links in a network, such as a social network, are disclosed. The existing network structure can be used to optimize link prediction. The methods and systems can learn a distance metric and/or a degree preference function that are structure preserving to predict links for new/existing nodes based on node properties.

Abstract: A system and method are provided for associating location data from one or more unique sources. The place and time of a unique location enabled device are associated with stored demographic information relating to the particular place and particular time. The place and time of the unique location enabled device are associated with a historical record of past locations and time of locations that the device has been. Based on the association of demographical information and historical information, the unique location enabled device is assigned to one or more groups or tribes. The location of all members of the group or tribe can be aggregated and exported for further analysis or display, thereby showing all group or tribe members at a particular time and place.

Abstract: Entities may be matched to enhance the efficiency of various commercial activities using various system and method embodiments of the disclosed subject matter. Belief propagation on a graph data structure defining a bipartite or unipartite matching opportunity is used to calculate a best matching. In embodiments, functions are implemented based upon the match, such as executing sales between matched buyers and sellers in an online auction system. In embodiments, messages with scalar values carry information about the relative value of possible matchings, initially provided as weights or values for the possible matchings. Weights may depend on, for example, bids or costs. Messages may be passed, for example over a network between processors respective to the nodes. Belief values reflecting a best matching can be continuously updated for each node responsively to the value information and received messages to rank the matches respective to each node, which progressively improve.

Abstract: A computerized method for optimizing parameters is described. A system can initialize a group of parameters to respective values within a set of allowable models and bound a partition function across a number of variable pairs to generate a plurality of bounds. The system can also determine new values for the group of parameters that minimize a sum of the plurality of bounds. The system can set the group of parameters to the new values and optimize the parameters by iteratively performing the bounding, determining and setting. The system can stop optimizing when a termination condition is reached.

Abstract: A system, method and computer-readable medium for maximum a posteriori (MAP) estimation of a graphical model are disclosed. The MAP estimation process can include obtaining an encoded data message sent over a 4G cellular wireless network and generating a graphical model representation of the message. The graphical model can be converted into a nand Markov random field (NMRF). The MAP estimation process can also include determining whether the NMRF has a perfect graph structure, and solving for a MAP estimate configuration of the NMRF. The MAP estimation process can further include outputting the MAP estimate configuration, an indication of the MAP estimate configuration, and/or a result based on a combination of the MAP estimate configuration and the encoded data message (e.g., a decoded message).

Abstract: A method, system, computer program product and computer readable media for b-matching using sufficient selection belief propagation is disclosed. The belief propagation method, is adapted to use a simplified compressed message update rule and is suitable for use with distributed processing systems. Embodiments for online advertisement/search term matching, product recommendation, dating service and social network matching, auction buyer/seller matching and resource allocation, among others, are disclosed.

Abstract: A method, system, computer program product and computer readable media for matching using degree distribution information are disclosed. An embodiment of the method can include performing b-matching on a graph data structure expanded using degree distribution information in order to identify neighbors of a selected input node. The b-matching can be performed using belief propagation. The belief propagation method is adapted to use a compressed message update rule and to be suitable for use with distributed processing systems. An embodiment can also include enhancing a matching result by applying degree distribution information to a first matching result to generate a second matching result. Embodiments for online advertisement/search term matching, product recommendation, dating service and social network matching, auction buyer/seller matching and resource allocation, among other, are disclosed.

Abstract: A system, apparatus, method, and computer-readable medium for optimizing classifiers are disclosed. The optimization process can include receiving one or more training examples. The optimization process can further include assigning a loss parameter to each training example. The optimization process can further include optimizing each loss parameter of each training sample based on a sample variance of each training example using a non-linear function. The optimization process can further include estimating a classifier from the one or more weighted training samples. The optimization process can further include assigning a loss parameter to the classifier based on a number of training examples that the classifier correctly classified and a number of training examples that the classifier incorrectly classified. The optimization process can further include adding the weighted classifier to an overall classifier.

Abstract: A method, system, computer program product and computer readable media for matching using degree distribution information are disclosed. An embodiment of the method can include performing b-matching on a graph data structure expanded using degree distribution information in order to identify neighbors of a selected input node. The b-matching can be performed using belief propagation. The belief propagation method is adapted to use a compressed message update rule and to be suitable for use with distributed processing systems. An embodiment can also include enhancing a matching result by applying degree distribution information to a first matching result to generate a second matching result. Embodiments for online advertisement/search term matching, product recommendation, dating service and social network matching, auction buyer/seller matching and resource allocation, among other, are disclosed.

Abstract: A method of detecting an event anomaly includes receiving one or more data points, in which each data point represents a spatial or temporal event; associating a unique identifier with each of the one or more data points to obtain one or more individualized data points; distributing the one or more individualized data points across a grid, in which the grid includes one or more cells; determining an event likelihood ratio for one or more of the grid cells; identifying one or more event clusters, in which each event cluster includes one or more of the grid cells; and storing in a data repository an event cluster having a significance level above a threshold significance level.