Abstract: A software and/or hardware facility for managing the referral of electronic advertisements is described. The facility may be configured to provide advertisements, including coupons, to a referrer and to enable the referrer to forward the advertisements to recipients. The facility also tracks recipient responses to the forwarded advertisements (e.g., redemption of coupons, interactions with the advertisement, rejections of advertisements, etc.). The facility may also be configured to provide incentives and/or disincentives to referrers based on recipient responses to advertisements.

Abstract: A software and/or hardware facility for filtering advertisements based on variable contexts is described. The facility may ascertain whether incoming messages are advertisements and filter messages containing advertisements. Advertisements may be filtered for deletion, storage, and/or user alerts with or without storage. The user may be alerted to incoming advertisements either upon receipt or at a later time. Filtering and notification may be based on real-time context (e.g., mood, location, time, environment, device status, etc.). In addition, filtering may also be based on message context and other contexts. User feedback may also be employed to refine filtering algorithms.

Abstract: A software and/or hardware facility for managing online advertisement bidding processes is described. The facility selects advertisements for delivery to mobile device users based on advertiser bids. The advertisers may bid for presentation of their advertisements to users having certain spatio-temporal, user segment, and/or other characteristics. The facility may also select advertisements for delivery based on an estimated likelihood of a mobile device user's positive response to the advertisement. Advertiser and user feedback may also be employed to refine the process of selecting advertisements for delivery.

Abstract: A software and/or hardware facility for providing a mobile device enhanced shopping experience is disclosed. The facility may determine a user characteristic of a mobile device user and deliver information in response to a product query. The information may include product information, competitive pricing information, competitive product information, related product information, a product advertisement, and/or the like. Also, the product query may be based on barcode information, a barcode image, product information, a product image, and/or the like.

Abstract: A software and/or hardware facility for inferring user context and delivering advertisements, such as coupons, using natural language and/or sentiment analysis is disclosed. The facility may infer context information based on a user's emotional state, attitude, needs, or intent from the user's interaction with or through a mobile device. The facility may then determine whether it is appropriate to deliver an advertisement to the user and select an advertisement for delivery. The facility may also determine an appropriate expiration time and/or discount amount for the advertisement.

Abstract: A user interface display for software has a user satisfaction portion displayed on each page. The user satisfaction portion includes a user selectable element which allows a user to provide a user satisfaction score (or level) with a single mouse click. In response to receiving the user satisfaction level, the context of the software which the user is using is recorded, and the user satisfaction level is correlated to that context. The captured data can be provided to application designers and developers directly or via computed metrics.

Abstract: Domain specific topics, and optionally uniform resource locators (URLs) can be received from a user, and from those domain specific topics and URLs, domain tuned search definitions are generated for a given domain. The domain tuned search definitions are saved and the user is provided with a definition of a domain tuned search interface that is embedded on a site specified by the user. When someone reviewing the user's web site performs a search using the domain tuned, embedded search interface, a search engine is invoked which performs a search on the user's input query, and then returns domain specific search results. The search engine searches for domain specific search results over web sites in addition to the web site that the user is currently reviewing, so the search is more precise than a general web search but broader than a specific site search.

Abstract: A machine learning system learns models to assist providers in processing documents of customers. Providers may use various productivity tools that use the learned models to assist in performing tasks on target documents of customers. The machine learning system may initially train models based on demographic information of customers and training data of the customers. To generate the models, the machine learning system collects the training data for the customers of each cluster and then trains a model for each cluster. The machine learning system uses the models to perform tasks on documents of customers. A provider can then modify the results of the task. The machine learning system can use those modifications to adjust the models.

Abstract: A recommendation system is provided that recommends tasks to be performed on electronic documents, providers for performing tasks on documents, and prices for tasks that are to be performed on the documents. In some embodiments, the recommendation system works in conjunction with a task brokerage system provided by a broker that helps customers who need tasks to be performed on documents to identify providers who can perform the requested tasks. The task brokerage system allows customers to publish their tasks and providers to discover the published tasks. The discovery process may match providers to customers based on criteria such as reputation, pricing, and availability.

Abstract: An information dissemination system identifies the interests of a user from information derived from various sources, such as, by way of example, the user's calendar entries, emails, World Wide Web (web) usage information, documents, enterprise resource planning (ERP) data, and the like. The information dissemination system then creates a profile for the user, and includes in the profile the details about the user's interests including the information sources to query, the user events which were analyzed to determine the user's interest, the topics derived from the analyzed events, the queries generated for the analyzed events, a language model of terms that are relevant to the user, and other information regarding the user. The information dissemination system then periodically executes the queries against the appropriate information sources, ranks the search results obtained from executing the queries, and renders the ranked search results to the user.

Abstract: Text classification has become an important aspect of information technology. Present text classification techniques range from simple text matching to more complex clustering methods. Clustering describes a process of discovering structure in a collection of characters. The invention automatically analyzes a text string and either updates an existing cluster or creates a new cluster. To that end, the invention may use a character n-gram matching process in addition to other heuristic-based clustering techniques. In the character n-gram matching process, each text string is first normalized using several heuristics. It is then divided into a set of overlapping character n-grams, where n is the number of adjacent characters. If the commonality between the text string and the existing cluster members satisfies a pre-defined threshold, the text string is added to the cluster. If, on the other hand, the commonality does not satisfy the pre-defined threshold, a new cluster may be created.

Abstract: Refining a user query is disclosed. In one method, a query is received from a user, and then mapped to one or more search concepts. A list of search concepts associated associated with the query is then displayed. Alternatively or additionally, the search concepts associated with the query are used to provide a set of improved search results. In another method, a number of queries from a number of users are analyzed to identify two or more search concepts, and a popularity value is assigned to them based on the queries. Thus, the relative popularity of the respective search concepts can be determined. Alternatively or additionally, a preferred search query for the search concepts can be determined. The popularity and preferred queries can be used to allow automatic or user-initiated refinement.

Abstract: The claimed subject matter provides systems and/or techniques that effectuate combining domain-tuned search systems. The system can include mechanisms that obtain queries, written descriptions, or illustrative web-pages regarding a particular area of interest, and generate a definition related to the area of interest. The definition contains a list of paths with associated weights employed to identify an pre-established first domain-tuned search system related to the area of interest. The first domain-tuned search system thereafter can be combined with a second domain-tuned search system related to another area of interest and presented to a user for utilization in re-ranking generic search results to be specific to the first and second domains of interest if combined, or to only the first domain if weights for the second domain are logically subtracted.

Abstract: A human assisted method of debugging training data used to train a machine learning classifier is provided. The method includes obtaining a classifier training data set. The training data set is then debugged using an integrated debugging tool configured to implement a debugging loop to obtain a debugged data set. The debugging tool can be configured to perform an estimation and simplification step to reduce data noise in the training data set prior to further analysis. The debugging tool also runs a panel of prediction-centric diagnostic metrics on the training data set, and provides the user prediction based listings of the results of the panel of prediction-centric diagnostic metrics.

Abstract: A system that facilitates performance of a focused search over a collection of sites comprises a subweb that corresponds to a topic and/or user characteristic(s) that are of interest to the user. The subweb includes a plurality of domains and/or paths (e.g. sites) that are related to the topic and/or the user characteristic(s). Each of the sites within the subweb is assigned a weight that indicates relevance of the site to the desirable topic and/or user characteristic(s). A search engine employs the subweb to facilitate focusing a search over a collection of sites. The search engine receives a query, and utilizes the subweb to focus a search over the selection of sites corresponding to the topic and/or user characteristic(s) represented by the subweb. The results from the search are returned to the user based at least in part upon the relevance weights assigned to the sites within the subweb.

Abstract: An information dissemination system identifies the interests of a user from information derived from various sources, such as, by way of example, the user's calendar entries, emails, World Wide Web (web) usage information, documents, enterprise resource planning (ERP) data, and the like. The information dissemination system then creates a profile for the user, and includes in the profile the details about the user's interests including the information sources to query, the user events which were analyzed to determine the user's interest, the topics derived from the analyzed events, the queries generated for the analyzed events, a language model of terms that are relevant to the user, and other information regarding the user. The information dissemination system then periodically executes the queries against the appropriate information sources, ranks the search results obtained from executing the queries, and renders the ranked search results to the user.

Abstract: An information dissemination system provides users information related to topics extracted from event classes. Event classes can generally be thought of as scripts that specify topics and actions for obtaining information related to the topics. The event classes are executed to obtain information related to the particular event class. The information dissemination system identifies a user event that is of interest to a user. The information dissemination system then identifies an event class that corresponds to the user event. The information dissemination system then performs the actions specified for the event class, obtains the action results, and renders the action results to the user.

Abstract: An information dissemination system ranks the search results based on a temporal weight assigned to each search result. The temporal weight is an indication of the importance to a user that varies with time. For each search result, the information dissemination system calculates a temporal weight that is based on the temporal proximity of the event that is related to the search result. The temporal weight may be used to re-rank the search results.

Abstract: A system and methods are provided for runtime spelling analysis and correction in a computing system. Misspelled entries or input text is automatically corrected turning the misspelled entries or input text into valid entries or text. The words used for spell checking and correction may be collected through multiple channels or from multiple sources, including words commonly found on the Web, and in users' entries or input text, as well as words from a standard language lexicon, all of which may be in one or more languages. The word(s) are automatically corrected only when there is a very high confidence that the correction is desirable. In various embodiments, the system implements a plurality of mechanisms with which the user can override the correction if invoked.

Abstract: The present invention relates to a system and methodology that applies automated learning procedures for determining document relevance and assisting information retrieval activities. A system is provided that facilitates a machine-learned approach to determine document relevance. The system includes a storage component that receives a set of human selected items to be employed as positive test cases of highly relevant documents. A training component trains at least one classifier with the human selected items as positive test cases and one or more other items as negative test cases in order to provide a query-independent model, wherein the other items can be selected by a statistical search, for example. Also, the trained classifier can be employed to aid an individual in identifying and selecting new positive cases or utilized to filter or re-rank results from a statistical-based search.