Abstract: Described herein is a method that includes receiving user history data and generating an indication of quality of a tier assignment used to store searchable digital items in a tiered storage system, wherein the indication is based at least in part upon a subset of the user history data. Also described herein is a system that includes a receiver component that receives user history data. The system further includes a quality indicator component that determines an indication of quality of a tier assignment used to store digital items that are retrievable by way of querying, wherein the quality indicator component generates the indication based at least in part upon a subset of the user history data and the tier assignment indicates where digital items are to be stored in a tiered storage system.

Abstract: Systems, methods, and computer storage media having computer-executable instructions embodied thereon that utilize relevant user profile data for advertisement selection. An advertisement context string is referenced. One or more related context strings that are related to the advertisement context string are identified. The advertisement context string and the one or more related context strings are compared to user profile strings associated with a user. It is determined that at least one of the advertisement context string or the one or more related context keywords is similar to at least one of the user profile strings. Any of the similar strings can be utilized to select an advertisement to be presented to the user.

Abstract: A method disclosed herein includes accessing a data repository that comprises an item advertisement profile for a user, wherein the item advertisement profile for the user comprises data pertaining to items of interest to the user and pricing preferences that correspond to at least one item of interest to the user. The method further includes selecting an item advertisement for a particular item from a plurality of possible item advertisements based at least in part upon the item advertisement profile for the user and causing the advertisement for the particular item to be displayed to the user on a display screen of a computing device.

Abstract: A two-phase process quickly and accurately identifies representations of the same items within a collection of item representations. In the first phase, referred to as a “blocking phase,” frequency information indicating the frequency with which terms appear within the collection of item representations is used to quickly identify “candidate pairs” (i.e., pairs of item representations that have a relatively high probability of matching). The blocking phase results in a reduced subset of the data for further analysis during the second phase. In the second phase, referred to as a “matching phase,” the candidate pairs are analyzed using fuzzy matching functions to accurately identify “matching pairs” (i.e., representations of the same items).

Abstract: A source keyword may be received multiple times and each time, in response, a machine-learning algorithm may be used to identify and rank respective matching-keywords that have been determined to match the source keyword. A portion or unit of content may be generated based on one of the ranked matching-keywords. The content is transmitted via a network to a client device and a user's impression of the content is recorded. The machine-learning algorithm may continue to rank matching-keywords for arbitrary source keywords while the recorded impressions and corresponding matched-keywords, respectively, are used to train the machine-learning algorithm. The training alters how the machine-learning algorithm ranks matching-keywords determined to match the source keyword.

Abstract: It has been found that user navigation that follows search engine interactions provides implicit endorsement of resources (such as web resources) that are preferred by users, and which may be particularly valuable for exploratory search tasks. Thus, a combination of past searching and browsing user behavior is analyzed to identify additional information that augments search results delivered by a search engine. The additional information may include a display of hyperlinks to locations which are derived from the past searching and browsing user behavior, given a specific input query. The additional information may be provided to supplement web search results.

Abstract: A method and system to produce and train composite similarity functions for record linkage problems, including product normalization problems, is disclosed. In one embodiment, for a group of products in a plurality of products, a composite similarity function is constructed for the group of products from a weighted set of basis similarity functions. Training records are used to calculate the weights in the weighted set of basis similarity functions in the composite similarity function for the group of products. In another embodiment, a composite similarity function is applied to pairs of training records. The application of the composite similarity function provides a number that can be used to indicate whether two records relate to a common subject. The composite similarity function includes a weighted set of basis similarity functions. A perceptron algorithm is used to modify the weights in the weighted set.

Abstract: A component presents access to a recommended search provider via a user interface element. In an example embodiment, a device-implemented method for recommending a search provider includes acts of receiving, ascertaining, and modifying. A search query input is received. A recommended search provider is ascertained at least partially responsive to the search query input. A user interface is modified to indicate the recommended search provider.

Abstract: Aspects of the subject matter described herein relate to predicting a best search engine to use for a given query. In aspects, a predictor may use various approaches to determine a best search engine for a given query. For example, the predictor may use features derived from the query itself, how well the query matches a result set returned by a search engine in response to the query, and/or information that compares the result sets returned by multiple search engines that are provided the query. In addition, other data such as user preferences, user interaction data, metadata attributes, and/or other data may be used in predicting a best search engine for a given query. In conjunction with making a prediction, the predictor may use a classifier that has been trained at a training facility.

Abstract: A system that facilitates targeted advertising is described in detail herein. The system includes a receiver component that receives user data that includes historical searching and browsing activity of a user. A profile generator component generates a user profile based at least in part upon a subset of the user data, wherein the user profile includes a plurality of keywords, wherein at least one keyword in the plurality of keywords is assigned a score that is indicative of a probability that an advertisement corresponding to the keyword will be monetized.

Abstract: A relevant information source identification technique that exploits a combination of searching and browsing activity of many users to identify relevant resources for future queries. The technique relies on such data to identify relevant information sources for new queries. In one embodiment, the technique is term-based: past queries are decomposed into individual (possibly overlapping) terms and phrases, and the most relevant documents are identified for each phrase from the browsing patterns of users that follow the query. Then, for a new query that consists of several terms or phrases, the most relevant destinations for each term/phrase are combined to produce overall predictions of the best or most relevant sources for the new query. This allows for providing predictions for previously unseen queries, which comprise a large proportion of the overall query volume.

Abstract: Described herein is a system that includes a receiver component that receives an indication that a user has accessed a search engine to initiate a search session. An analyzer component predicts informational interests of the user upon receipt of the indication and outputs an informational item that corresponds to the predicted informational interests of the user, wherein the analyzer component is configured to output the informational item prior to the user issuing a query to the search engine.

Abstract: Described herein is a method that includes receiving user history data and generating an indication of quality of a tier assignment used to store searchable digital items in a tiered storage system, wherein the indication is based at least in part upon a subset of the user history data. Also described herein is a system that includes a receiver component that receives user history data. The system further includes a quality indicator component that determines an indication of quality of a tier assignment used to store digital items that are retrievable by way of querying, wherein the quality indicator component generates the indication based at least in part upon a subset of the user history data and the tier assignment indicates where digital items are to be stored in a tiered storage system.

Abstract: A search query transformation system and method for transforming and refining a search query are described. Embodiments of the system and method use various graphical components and controls. Direct manipulation ensures that the searcher is driving the changes in the search queries using a pointing device. Embodiments of the search query transformation system and method include a search query re-weighting user interface (UI) component for graphically adjusting and re-weighting weights of search terms, and a search query term replacement UI component for graphically replacing a search term in a query or add a synonym to the query. Embodiments of the system and method also include a search query suggestion component, which provides query revision recommendations to a searcher that are tailored to the direct manipulation query refinement interface.

Abstract: A two-phase process quickly and accurately identifies representations of the same items within a collection of item representations. In the first phase, referred to as a “blocking phase,” frequency information indicating the frequency with which terms appear within the collection of item representations is used to quickly identify “candidate pairs” (i.e., pairs of item representations that have a relatively high probability of matching). The blocking phase results in a reduced subset of the data for further analysis during the second phase. In the second phase, referred to as a “matching phase,” the candidate pairs are analyzed using fuzzy matching functions to accurately identify “matching pairs” (i.e., representations of the same items).

Abstract: It has been found that user navigation that follows search engine interactions provides implicit endorsement of resources (such as web resources) that are preferred by users, and which may be particularly valuable for exploratory search tasks. Thus, a combination of past searching and browsing user behavior is analyzed to identify additional information that augments search results delivered by a search engine. The additional information may include a display of hyperlinks to locations which are derived from the past searching and browsing user behavior, given a specific input query. The additional information may be provided to supplement web search results.