Abstract: Systems and methods to perform short text segment similarity measures. Illustratively, a short text segment similarity environment comprises a short text engine operative to process data representative of short segments of text and an instruction set comprising at least one instruction to instruct the short text engine to process data representative of short text segment inputs according to a selected short text similarity identification paradigm. Illustratively, two or more short text segments can be received as input by the short text engine and a request to identify similarities among the two or more short text segments. Responsive to the request and data input, the short text engine executes a selected similarity identification technique in accordance with the sort text similarity identification paradigm to process the received data and to identify similarities between the short text segment inputs.

Abstract: Statistical approaches are employed to estimate available inventory for opt-in email marketing mailings. A random sample is selected from an overall population of subscribers who have opted-in to receiving email marketing messages. When a query is received for determining available inventory that satisfy some target criteria, the random sample is employed to estimate the available inventory in the overall population taking into account inventory that has been consumed by one or more other orders for email marketing mailings.

Abstract: The present invention utilizes a cross-prediction scheme to predict values of discrete and continuous time observation data, wherein conditional variance of each continuous time tube variable is fixed to a small positive value. By allowing cross-predictions in an ARMA based model, values of continuous and discrete observations in a time series are accurately predicted. The present invention accomplishes this by extending an ARMA model such that a first time series “tube” is utilized to facilitate or “cross-predict” values in a second time series tube to form an “ARMAxp” model. In general, in the ARMAxp model, the distribution of each continuous variable is a decision graph having splits only on discrete variables and having linear regressions with continuous regressors at all leaves, and the distribution of each discrete variable is a decision graph having splits only on discrete variables and having additional distributions at all leaves.

Abstract: A user utility function is implemented in allocating advertisement space to one or more potential advertisers. The user utility function allows advertisement space to be allocated based upon, among other things, the expected utility or usefulness that a proposed advertisement will have to a user. The user utility function, for example, compares proposed advertisements to historical user actions to generate respective user utility values for advertisements (e.g., based upon user responses to advertisements for particular types of product, responses to advertisements from particular types of sellers, etc.). The user utility values can then be applied to bids submitted by advertisers for advertisement space for particular advertisements to obtain modified bids. The modified bids thus reflect, among other things, the expected utility of an advertisement to a user, and thus allow an advertisement host to allocate advertisement space accordingly.

Abstract: A publisher union comprises a plurality of publishers, a channel, and a publisher union administrator. The publisher union is administered by receiving a channel proposal, determining whether the channel proposal is acceptable, forming a channel, and presenting the channel for monetization. User information is gathered by the publisher union by establishing a domain, collecting user information, aggregating the user information, and providing the aggregated user information to publisher union members.

Abstract: An interactive game within a virtual world platform is provided wherein a sender can hide an object within a mapping application. An intended recipient of the object can be notified that the object is available and a clue can be provided. The clue can direct the recipient in searching for where the object is hidden. The objects can be intended for a single recipient, multiple recipients, or open to everyone on a first-come-first-served basis. The objects can be stolen by others and/or the objects can be re-gifted after being found. After being found, the virtual object can be automatically or manually converted into a non-virtual object.

Abstract: The present invention utilizes a discriminative density model selection method to provide an optimized density model subset employable in constructing a classifier. By allowing multiple alternative density models to be considered for each class in a multi-class classification system and then developing an optimal configuration comprised of a single density model for each class, the classifier can be tuned to exhibit a desired characteristic such as, for example, high classification accuracy, low cost, and/or a balance of both. In one instance of the present invention, error graph, junction tree, and min-sum propagation algorithms are utilized to obtain an optimization from discriminatively selected density models.

Abstract: The claimed subject matter provides systems and/or methods that effectuate generation of alternative expressions or phrasings for short descriptions, proper nouns or places. The system can include devices that select and associate an item with a prompt, displays the selected item and then obscures the item with the prompt associated with the item, elicits a response from users to the prompt based on a motivational statement constructed to solicit an appropriate response from the user. The response elicited from the user and the item selected associated with one another and then persisted to storage media.

Abstract: A hide and seek style game is utilized to elicit human input for use in improving search. Content (e.g., text, image, audio, video . . . ) is uniquely identified and revealed to users. Queries are then specified by users in an attempt to locate the content. In addition to utilizing these queries to return results for the game, the queries, and/or query-derived information, can also be employed to improve search engine retrieval and relevancy, among other things.

Abstract: Systems and methods allow an on-line game to extract information relevant to a specific need of a game platform or service platform. The specific need relates to management and use of digital content, and is addressed by designing and playing an on-line collaborative game. The rules of the game intend to solve a specific task dictated by the specific need. Players' responses to the game generate a wealth of information related to a specific task objective, such as ranking, sorting, and evaluating a set of digital content items. To compel participation in a game, players can be rewarded with monetary value rewards. As a game illustration, an image selection game (ISG) that exploits human contextual inference is described in detail. The information extracted from ISG is a list of key-image associations, relevant for the task of image sorting and ranking.

Abstract: A media recommendation and sharing technique that employs agents on media players/devices to expand the scope of media sharing scenarios. The technique assists a user in discovering media items, such as, for example, music, recordings, play lists, pictures, video games, on nearby media players or devices (devices which are capable of receiving, storing and playing media) which are interesting to the user. The collaborative media recommendation and sharing technique contemporaneously determines a user's media preferences based on media stored on a pair of media devices and recommends media for potential sharing based on these determined user preferences.

Abstract: The subject invention leverages scaleable itemsets and/or association rules to provide dynamic adjustment of memory usage. This allows the subject invention to provide association rules and/or itemsets with the highest support while utilizing a bounded amount of memory. Thus, a data analysis system and/or method utilizing the subject invention can self-adjust to provide the best association rules and/or itemsets based on available system resources. One instance of the subject invention employs dynamically adjustable minimum support values for data itemsets and/or association rules to facilitate in compensating for memory availability. In yet another instance of the subject invention a prefix tree data structure is utilized to facilitate in constructing itemsets. Memory utilization is then adjusted via pruning and/or reallocation of counter vectors and/or pointer vectors and/or reallocation of nodes of the prefix tree data structure for scaleable data itemsets and/or association rules.

Abstract: A random walk is performed over a graph, such as an augmented bipartite graph, relating to ownership data with respect to a plurality of users and items owned; the graph can provide social links between the users as well. Items can be recommended to users who do not own the items by randomly walking the graph starting at the user node to which the recommendation will be given. The random walk can step from user to user or from user to item; when an item is reached, the node can be absorbing such that the random walk terminates. The arrived item is recommended to the user. Parameters can also be provided to affect decisions made during the walk about which users to walk to and/or whether to walk to a user or an item.

Abstract: Providing for automated generation of tags (e.g., metadata descriptors) for items of e-mail or syndication formatted communication is described herein. By way of example, a system can include a filtering component that can generate one or more tags based on information relevant to content of the communication, a sender, or recipient, or combinations thereof. In addition, such tags can be automatically attached to a received item, or a presentation component can furnish the tags to a recipient (e.g., by way of a communication device user interface) for selection, whereby selected tags are associated with the item of communication. Accordingly, the subject innovation provides for improved classification and description of items of communication by automatic generation of descriptive and/or representative tags associated therewith.

Abstract: Systems and methods that enrich interaction of users in a written communication environment by providing a contextual language component that points to a designated data source (e.g., an object) and pulls data from such designated source, to perform predetermined function(s) thereupon. Users who communicate in the written environment can type an intuitive or customized operator, to engage in a predetermined functionality (e.g., playing a video). Accordingly, the operator can readily be interpreted by applications associated with the written communication environment.

Abstract: The present invention utilizes generic and user-specific features of handwriting samples to provide adaptive handwriting recognition with a minimum level of user-specific enrollment data. By allowing generic and user-specific classifiers to facilitate in a recognition process, the features of a specific user's handwriting can be exploited to quickly ascertain characteristics of handwriting characters not yet entered by the user. Thus, new characters can be recognized without requiring a user to first enter that character as enrollment or “training” data. In one instance of the present invention, processing of generic features is accomplished by a generic classifier trained on multiple users. In another instance of the present invention, a user-specific classifier is employed to modify a generic classifier's classification as required to provide user-specific handwriting recognition.

Abstract: Visualizing Internet web traffic is disclosed. In one embodiment, a number of windows are displayed, corresponding to a number of clusters into which users have been partitioned based on similar web browsing behavior. The windows are ordered from the cluster having the greatest number of users to the cluster having the least number of users. Each window has one or more rows, where each row corresponds to a user within the cluster. Each row has an ordered number of visible units, such as blocks, where each block corresponds to a web page visited by the user. The blocks can be color coded by the type of web page they represent. In one embodiment, the corresponding cluster models for the clusters are alternatively displayed in the windows.

Abstract: The present invention provides collaborative filtering systems and methods employing default scores of decision graphs/trees to quickly create a top-n prediction list that can efficiently determine a user's interest in items. In one instance of the present invention, the list is refined by utilizing a variable maximum score algorithm and/or an invalidation list algorithm to insert items that score above an inclusion threshold set by a last item in the top-n prediction list. In another instance of the present invention, an invalidation list for a decision graph and/or decision tree is utilized to create a top-n prediction list. An algorithm employing default scores is then utilized to refine the top-n prediction list to insert items with default scores above an inclusion threshold set by a last item in the top-n prediction list.

Abstract: The subject invention leverages data sampling techniques to provide an efficient means to determine co-occurrence count estimations for objects and features from relational data, simplifying measure-of-association determinations. By providing an efficient mechanism to estimate co-occurrence counts, instances of the subject invention can be incorporated directly into a database, increasing its versatility and performance for such uses as collaborative filtering recommendations. Other instances of the subject invention can be utilized for enhancing database query selectivity, optimizing query performance through employment of á priori co-occurrence counts.

Abstract: The subject invention leverages the conditional Gaussian (CG) nature of a continuous variable stochastic ARMAxp time series model to efficiently determine its parametric gradients. The determined gradients permit an easy means to construct a parametric structure for the time series model. This provides a gradient-based alternative to the expectation maximization (EM) process for learning parameters of the stochastic ARMAxp time series model. Thus, gradients for parameters can be computed and utilized with a gradient-based learning method for estimating the parameters. This allows values of continuous observations in a time series to be predicted utilizing the stochastic ARMAxp time series model, providing efficient and accurate predictions.