Abstract: User generated reviews and scores associated with the reviews may be used to train a review scoring model with textual features of the reviews. The review scoring model may be used to predict scores for newly received reviews. One or more constraints based on social networking application data associated with an author of a review may be used to adjust the predicted score of the review.

Abstract: A tree structure has a node associated with each category of a hierarchy of item categories. Child nodes of the tree are associated with sub-categories of the categories associated with parent nodes. Training data including received queries and indicators of a selected item category for each received query is combined with the tree structure by associating each query with the node corresponding to the selected category of the query. When a query is received, a classifier is applied to the nodes to generate a probability that the query is intended to match an item of the category associated with the node. The classifier is applied until the probability is below a threshold. One or more categories associated with the nodes that are closest to the intent of the received query are selected and indicators of items of those categories that match the received query are output.

Abstract: A query may be received at a computing device through a network. One or more attribute values that are preferences for a subset of the one or more terms of the query may be identified by the computing device. One or more products or services having associated attributes that have values that match a subset of the identified attribute values may be identified by the computing device, and a subset of the identified products or services may be presented by the computing device through the network. Implementations may also identify latent preferences, that is, preferences that are found for a query even where such a preference is not explicitly part of a term or token of the query.

Abstract: Techniques are provided for selecting a limited but comprehensive set of high-quality users reviews covering several different aspects or attributes of a reviewed item. For several implementations, selection methodologies approach the challenge as a maximum coverage problem and provide a generic formalism to model the different variants of the review-set selection. Variations to such implementation may also employ different algorithms in consideration of different variants and weightings of those variants. Select implementations employ methodologies that collectively consider attributes of the item discussed in the reviews, the quality of the reviews themselves, and the viewpoint of the reviews (e.g., positive or negative) as input values in order to provide outputs that cover as many attributes of the item as possible, comprising high quality reviews representing different viewpoints.

Abstract: As provided herein objects from a source catalog, such as a provider's catalog, can be added to a target catalog, such as an enterprise master catalog, in a scalable manner utilizing catalog taxonomies. A baseline classifier determines probabilities for source objects to target catalog classes. Source objects can be assigned to those classes with probabilities that meet a desired threshold and meet a desired rate. A classification cost for target classes can be determined for respective unassigned source objects, which can comprise determining an assignment cost and separation cost for the source objects for respective desired target classes. The separation and assignment costs can be combined to determine the classification cost, and the unassigned source objects can be assigned to those classes having a desired classification cost.

Abstract: Previously received queries from a search log are analyzed to determine a category of structured data associated with each query. For example, the categories may correspond to consumer product categories such as televisions, digital cameras, etc. For each category, the terms of the queries associated with the category are correlated with the attributes and attribute values of the structured data tuples associated with the category. The attributes may be ranked based on the correlation. When a subsequent query is received, the category of the query is determined and the ranked attributes associated with the category are used to select facets that are displayed to the user along with the search results.

Abstract: A tree structure has a node associated with each category of a hierarchy of item categories. Child nodes of the tree are associated with sub-categories of the categories associated with parent nodes. Training data including received queries and indicators of a selected item category for each received query is combined with the tree structure by associating each query with the node corresponding to the selected category of the query. When a query is received, a classifier is applied to the nodes to generate a probability that the query is intended to match an item of the category associated with the node. The classifier is applied until the probability is below a threshold. One or more categories associated with the nodes that are closest to the intent of the received query are selected and indicators of items of those categories that match the received query are output.

Abstract: A method and apparatus for ranked join indices includes a solution providing performance guarantees for top-k join queries over two relations, when preprocessing to construct a ranked join index for a specific join condition is permitted. The concepts of ranking join indices presented herein are also applicable in the case of a single relation. In this case, the concepts herein provide a solution to the top-k selection problem with monotone linear functions, having guaranteed worst case search performance for the case of two ranked attributes and arbitrary preference vectors.

Abstract: A query may be received at a computing device and may include one or more terms. For each set of structured data tuples, a set of tokens may be determined from the terms of the query by the computing device based on attribute values of attributes associated with the structured data tuples in the set of structured data tuples. An annotated query may be determined from each of the sets of tokens. A probability score may be determined for each of the determined annotated queries. The annotated query having the highest determined probability score may be selected, and one or more structured data tuples may be identified from the structured data tuples that have attributes with attribute values that match one or more tokens of the selected annotated query.

Abstract: User generated reviews and scores associated with the reviews may be used to train a review scoring model with textual features of the reviews. The review scoring model may be used to predict scores for newly received reviews. One or more constraints based on social networking application data associated with an author of a review may be used to adjust the predicted score of the review.

Abstract: A query may be received at a computing device through a network. One or more attribute values that are preferences for a subset of the one or more terms of the query may be identified by the computing device. One or more products or services having associated attributes that have values that match a subset of the identified attribute values may be identified by the computing device, and a subset of the identified products or services may be presented by the computing device through the network. Implementations may also identify latent preferences, that is, preferences that are found for a query even where such a preference is not explicitly part of a term or token of the query.

Abstract: Methods are provided for the classification of search engine queries and associated documents based on search engine query click logs. One or more seed documents or queries are provided that contain content that is representative of a category. A query click log containing information regarding queries entered by at least one user into the search engine and documents subsequently clicked in search engine results corresponding with the queries is analyzed to determine which one or more queries resulted in clicks on the seed documents. Information is stored associating the one or more queries with the category if they resulted in clicks on the seed documents.

Abstract: As provided herein objects from a source catalog, such as a provider's catalog, can be added to a target catalog, such as an enterprise master catalog, in a scalable manner utilizing catalog taxonomies. A baseline classifier determines probabilities for source objects to target catalog classes. Source objects can be assigned to those classes with probabilities that meet a desired threshold and meet a desired rate. A classification cost for target classes can be determined for respective unassigned source objects, which can comprise determining an assignment cost and separation cost for the source objects for respective desired target classes. The separation and assignment costs can be combined to determine the classification cost, and the unassigned source objects can be assigned to those classes having a desired classification cost.

Abstract: A method and apparatus for ranked join indices includes a solution providing performance guarantees for top-k join queries over two relations, when preprocessing to construct a ranked join index for a specific join condition is permitted. The concepts of ranking join indices presented herein are also applicable in the case of a single relation. In this case, the concepts herein provide a solution to the top-k selection problem with monotone linear functions, having guaranteed worst case search performance for the case of two ranked attributes and arbitrary preference vectors.

Abstract: A method and apparatus for ranked join indices includes a solution providing performance guarantees for top-k join queries over two relations, when preprocessing to construct a ranked join index for a specific join condition is permitted. The concepts of ranking join indices presented herein are also applicable in the case of a single relation. In this case, the concepts herein provide a solution to the top-k selection problem with monotone linear functions, having guaranteed worst case search performance for the case of two ranked attributes and arbitrary preference vectors.

Abstract: Data from a click log may be used to generate training data for a search engine. The pages clicked as well as the pages skipped by a user may be used to assess the relevance of a page to a query. Labels for training data may be generated based on data from the click log. The labels may pertain to the relevance of a page to a query.

Abstract: A social network may be used to determine a rating of a user with no prior history. The ratings for unrated nodes may be inferred from the existing ratings of users associated with the unrated node in either or both the underlying social network or other social networks. Additionally in some implementations, the effect of the rating of a rated node to an unrated node diminishes as the strength of their relationships decreases. In some cases, a social network may be modeled as an electrical network, and ratings may be modeled as voltages on the nodes of the social network, relationships in the social network may be modeled as connections in the electrical network, and in some cases the strength of relationships may be modeled as conductance of the connections. Ratings for nodes may be determined using Kirchhoff's Law and in some cases by solving a set of linear equations or by propagating positive and negative ratings using a random walk with absorbing states.

Abstract: Methods are provided for the classification of search engine queries and associated documents based on search engine query click logs. One or more seed documents or queries are provided that contain content that is representative of a category. A query click log containing information regarding queries entered by at least one user into the search engine and documents subsequently clicked in search engine results corresponding with the queries is analyzed to determine which one or more queries resulted in clicks on the seed documents. Information is stored associating the one or more queries with the category if they resulted in clicks on the seed documents.

Abstract: A method and apparatus for ranked join indices includes a solution providing performance guarantees for top-k join queries over two relations, when preprocessing to construct a ranked join index for a specific join condition is permitted. The concepts of ranking join indices presented herein are also applicable in the case of a single relation. In this case, the concepts herein provide a solution to the top-k selection problem with monotone linear functions, having guaranteed worst case search performance for the case of two ranked attributes and arbitrary preference vectors.

Abstract: This invention includes the step of transmitting a query to a set of search engines. Any result lists returned from these search engines is received, and a subset of entries in each result list is selected. Each entry in this subset is assigned a scoring value according to a scoring function, and each result list is then assigned a representative value according to the scoring values assigned to its entries. A merged list of entries is produced based upon the representative value assigned to each result list.