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: 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: Documents are replicated among servers comprising a search engine based on the value of each document by approximating its value as one of the top search results for one or more exemplary queries. Documents are allocated among servers comprising a search engine by calculating a relevance value for each document and then distributing the documents evenly to the servers. A subset of servers are selected from among a plurality of servers comprising a search engine using term-based, server-specific histograms reflecting the number of instances of the term in each document allocated to each server, and then selecting servers to service a query based on the documents on those servers.

Abstract: Documents are replicated among servers comprising a search engine based on the value of each document by approximating its value as one of the top search results for one or more exemplary queries. Documents are allocated among servers comprising a search engine by calculating a relevance value for each document and then distributing the documents evenly to the servers. A subset of servers are selected from among a plurality of servers comprising a search engine using term-based, server-specific histograms reflecting the number of instances of the term in each document allocated to each server, and then selecting servers to service a query based on the documents on those servers.

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: 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.