Abstract: A generic predictive argument model that can be applied to a set of shot values to predict a target slot value is provided. The generic predictive argument model can predict whether or not a particular value or item is the intended target of the user command given various features. A prediction for each of the slot values can then be normalized to infer a distribution over all values or items. For any set of slot values (e.g., contacts), a number of binary variable s are created that indicate whether or not each specific slot value was the intended target. For each slot value, a set of input features can be employed to predict the corresponding binary variable. These input features are generic properties of the contact that are “instantiated” based o n properties of the contact (e.g., contact-specific features). These contact-specific features can be stored in a user data store.

Abstract: Various embodiments provide techniques for auction flighting. In one or more embodiments, a control group and a test group are designated for participants who compete one to another in online auctions. An inclusive model may then be employed for testing of new conditions for auctions using the groups. In particular, multiple auctions can be conducted and/or simulated, such that control conditions are applied in auctions that do not include at least one member of the test group, and test conditions are applied in auctions having members from both the test group and the control group. A response to the test conditions can then be measured by analyzing behaviors of the participants in the auctions conducted with the control conditions in comparison to behaviors of participants in the auctions conducted with the test conditions.

Abstract: Various embodiments provide techniques for graph clustering. In one or more embodiments, a participation graph is obtained that represents relationships between entities. An auxiliary graph is constructed based on the participation graph. The auxiliary graph may be constructed such that the auxiliary graph is less dense than the participation graph and is therefore computationally less complex to analyze. Clusters in the auxiliary graph are determined by solving an objective function defined for the auxiliary graph. Clusters determined for the auxiliary graph may then be utilized to ascertain clusters in the participation graph that solve a related objective function defined for the participation graph.

Abstract: Methods and computer-readable media are provided for receiving keyword expansions from expansion providers and selecting a set of keyword expansions that are used for advertisement selection. Keyword expansions that correspond to a particular search query or text from a browsed web page are received from an expansion provider. Feature data is extracted from each keyword expansion, and may include properties of the keyword expansion or the expansion provider. A score is assigned to each keyword expansion, and based on the score, a set of keyword expansions is selected from the keyword expansions received from the expansion provider. The set of keyword expansions is used to select relevant advertisements for presentation to the user.

Abstract: Systems and methods for determining the value of bids placed by content providers for placement positions on a page, e.g., a web page, rendered according to a given context, for instance, the search results listing for a particular query initiated on a search engine web site, are provided. Additionally, systems and methods are provided for determining placement of content items, e.g., advertisements and/or images, on a rendered page relative to other content items on the page based upon bid value.

Abstract: Decision trees populated with classifier models are leveraged to provide enhanced spam detection utilizing separate email classifiers for each feature of an email. This provides a higher probability of spam detection through tailoring of each classifier model to facilitate in more accurately determining spam on a feature-by-feature basis. Classifiers can be constructed based on linear models such as, for example, logistic-regression models and/or support vector machines (SVM) and the like. The classifiers can also be constructed based on decision trees. “Compound features” based on internal and/or external nodes of a decision tree can be utilized to provide linear classifier models as well. Smoothing of the spam detection results can be achieved by utilizing classifier models from other nodes within the decision tree if training data is sparse. This forms a base model for branches of a decision tree that may not have received substantial training data.

Abstract: A dialog system training environment and method using text-to-speech (TTS) are provided. The only knowledge a designer requires is a simple specification of when the dialog system has failed or succeeded, and for any state of the dialog, a list of the possible actions the system can take. The training environment simulates a user using TTS varied at adjustable levels, a dialog action model of a dialog system responds to the produced utterance by trying out all possible actions until it has failed or succeeded. From the data accumulated in the training environment it is possible for the dialog action model to learn which states to go to when it observes the appropriate speech and dialog features so as to increase the likelihood of success. The data can also be used to improve the speech model.

Abstract: The subject disclosure pertains to systems and methods that facilitate detection of cloaked web pages. Commercial value of search terms and/or queries can be indicative of the likelihood that web pages associated with the keywords or queries are cloaked. Commercial value can be determined based upon popularity of terms and/or advertisement market value as established based upon advertising revenue, fees and the like. Commercial value can be utilized in conjunction with term frequency difference analysis to identify a cloaked page automatically. In addition, commercial values of terms associated with web pages can be used to order or prioritize web pages for further analysis.

Abstract: Improved browsing experience in social bookmarking by leveraging aspects of self tagging and prediction. Quality recommendations are provided for sites of interest to the user and information about what types of people like the current website. Self-tagging is used as an effective means to perform personalized searches. Machine learning and reasoning is employed to predict self-tags based on a website visited and/or website behavior, and self-tags associated with a website and/or webpage based on content of that website and/or webpage. The architecture can be embodied as a browser utility to leverage and extend social-bookmarking information. The utility facilitates the display of information related to a summary view of the users who liked/disliked the current page or website, a tag cloud associated with webpages, and a recommendation button that causes self-tag recommendations to be displayed and that recommends links based on the combination of user tags and content.

Abstract: A dependency network is created from a training data set utilizing a scalable method. A statistical model (or pattern), such as for example a Bayesian network, is then constructed to allow more convenient inferencing. The model (or pattern) is employed in lieu of the training data set for data access. The computational complexity of the method that produces the model (or pattern) is independent of the size of the original data set. The dependency network directly returns explicitly encoded data in the conditional probability distributions of the dependency network. Non-explicitly encoded data is generated via Gibbs sampling, approximated, or ignored.

Abstract: A game description language is provided for human computation games, as well as a game platform or generator component that can generate the code base for the game. The game description language and schema framework can be used to represent the game logic and synchronization patterns of a human computation game. The automated code generation tool takes a file, e.g., a file made from the above game description language, or the like, as an input and generates a code base for the corresponding human computation game. These tools allow a prototype of a human computation game to be generated within minutes.

Abstract: A system and method for prediction of a user goal for command/control of a personal device (e.g., mobile phone) is provided. The system employs statistical model(s) that can predict a command based, at least in part, on past user behavior (e.g., probability distribution over a set of predicates, and, optionally arguments). Further, the system can be employed with a speech recognition component to facilitate language modeling for predicting the user goal. The system can include predictive user models (e.g., predicate model and argument model) that receive a user input (e.g., utterance) and employ statistical modeling to determine the likely command without regard to the actual content of the input (e.g., utterance). The system employs features for predicting the next user goal which can be stored in a user data store. Features can capture personal idiosyncrasies or systematic patterns of usage (e.g., device-related, time-related, predicate-related, contact-specific and/or periodic features).

Abstract: This document describes image-based human interactive proofs (HIPs). In some cases these proofs may be used when a browser at a client is used to access resources from a web server. Before access to the resources is enabled, the client can be challenged by the web server with an image-based puzzle. The image-based puzzle is configured to enable distinctions to be made between human input and non-human input. Input to answer the image-based puzzle can be formed via the client and communicated to the web server. The web server receives the input from the client and selectively enables client access to the resources based upon the input. In at least some embodiments, the web server can make use of a community database that stores client answers to image-based puzzles to assist in distinguishing between human input and non-human input.

Abstract: An online dialog system and method are provided. The dialog system receives speech input and outputs an action according to its models. After executing the action, the system receives feedback from the environment or user. The system immediately utilizes the feedback to update its models in an online fashion.

Abstract: A system and method for online reinforcement learning is provided. In particular, a method for performing the explore-vs.-exploit tradeoff is provided. Although the method is heuristic, it can be applied in a principled manner while simultaneously learning the parameters and/or structure of the model (e.g., Bayesian network model). The system includes a model which receives an input (e.g., from a user) and provides a probability distribution associated with uncertainty regarding parameters of the model to a decision engine. The decision engine can determine whether to exploit the information known to it or to explore to obtain additional information based, at least in part, upon the explore-vs.-exploit tradeoff (e.g., Thompson strategy). A reinforcement learning component can obtain additional information (e.g., feedback from a user) and update parameter(s) and/or the structure of the model.

Abstract: Architecture for integrating and generating back-off grammars (BOG) in a speech recognition application for recognizing out-of-grammar (OOG) utterances and updating the context-free grammars (CFG) with the results. A parsing component identifies keywords and/or slots from user utterances and a grammar generation component adds filler tags before and/or after the keywords and slots to create new grammar rules. The BOG can be generated from these new grammar rules and can be used to process the OOG user utterances. By processing the OOG user utterances through the BOG, the architecture can recognize and perform the intended task on behalf of the user.

Abstract: Systems and methods for determining the value of bids placed by content providers for placement positions on a page, e.g., a web page, rendered according to a given context, for instance, the search results listing for a particular query initiated on a search engine web site, are provided. Additionally, systems and methods are provided for determining placement of content items, e.g., advertisements and/or images, on a rendered page relative to other content items on the page based upon bid value.

Abstract: The present invention provides a unique system and method that can employ machine learning techniques to automatically fill one or more fields across a diverse array of web forms. In particular, one or more instrumented tools can collect input or entries of form fields. Machine learning can be used to learn what data corresponds to which fields or types of fields. The input can be sent to a central repository where other databases can be aggregated as well. This input can be provided to a machine learning system to learn how to predict the desired outputs. Alternatively or in addition, learning can be performed in part by observing entries and then adapting the autofill component accordingly. Furthermore, a number of features of database fields as well as constraints can be employed to facilitate assignments of database entries to form values—particularly when the web form has never been seen before by the autofill system.

Abstract: A computer-implemented system that facilitates establishing price(s) associated with items comprises a clustering component that clusters a collection of non-identical items into one or more sets of non-identical items. A pricing component receives one or more of active bids and stored bids and simulates an auction of at least one non-identical item within one of the one or more sets of non-identical items as if the non-identical items within the set were identical to determine price(s) associated with the at least one non-identical item. For example, the items can be at least portions of search terms received by a search engine.

Abstract: A predictive model analysis system comprises a receiver component that receives predictive samples created by way of forward sampling. An analysis component analyzes a plurality of the received predictive samples and automatically determines whether a predictive model is reliable at a time range associated with the plurality of predictive sample, wherein the determination is made based at least in part upon an estimated norm associated with a forward sampling operator.