Abstract: It is possible to provide meaningful responses to queries using systems which consider usage of words in the queries when analyzing those queries and determining what information is possibly relevant. This approach can be applied in online shopping systems by identification of nouns or noun phrases reflecting products available through the system.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for computing a displayable size for a device, a default visible area for the device, and a number of pages of content based upon historical device click-through data and pagination data. A size of an advertising candidate set is determined. The advertising candidate set, containing one or more advertisements, is determined based in part upon a query. The advertising candidate set is categorized into two or more buckets. The advertisements within each bucket are ranked. Selected advertisements based upon the ranked advertisements to send to the device are determined.

Abstract: A searcher can be configured to improve relevance ranking of search results through iterative weighting of search ranking results. A Search Auto Categorizer (SAC) operates on a base query to return a probabilistic distribution of leaf categories of a taxonomy in which relevant products may reside. A Search Logic Unit (SLU) can compute a relevance of any particular leaf category to the base query. The SLU can then determine an initial relevance of a particular product to the query based on the probabilistic distribution and the relevance of leaf category to query. The SLU weights the relevance of a product to the query to establish an updated probabilistic distribution. The SLU then repeats the relevance and weighting until convergence upon a relevance list.

Abstract: It is possible to provide meaningful responses to queries using systems which consider usage of words in the queries when analyzing those queries and determining what information is possibly relevant. This approach can be applied in online shopping systems by identification of nouns or noun phrases reflecting products available through the system.

Abstract: It is possible to provide meaningful responses to queries using systems which consider usage of words in the queries when analyzing those queries and determining what information is possibly relevant. This approach can be applied in online shopping systems by identification of nouns or noun phrases reflecting products available through the system.

Abstract: A search engine can be configured to improve search times by implementing a parallel computing architecture. The index is split across a plurality of independent search nodes. A query is communicated to each of the parallel nodes and the query is searched in each independent node. The results from each node are routed to a federator that is configured to aggregate the results to a result set. The federator is configured to determine a subset of intermediate results to retrieve and aggregate from each of the independent nodes. The federator determines a number of results to retrieve from each of the nodes based at least in part on the number of nodes and the number of search results desired in the results set.

Abstract: A searcher can be configured to improve relevance ranking of search results through iterative weighting of search ranking results. A Search Auto Categorizer (SAC) operates on a base query to return a probabilistic distribution of leaf categories of a taxonomy in which relevant products may reside. A Search Logic Unit (SLU) can compute a relevance of any particular leaf category to the base query. The SLU can then determine an initial relevance of a particular product to the query based on the probabilistic distribution and the relevance of leaf category to query. The SLU weights the relevance of a product to the query to establish an updated probabilistic distribution. The SLU then repeats the relevance and weighting until convergence upon a relevance list.

Abstract: Computer software, systems and methods for providing context personalized browsing over computer networks. In the foregoing, an intelligent agent referred to as a “browser companion agent” includes a service component for holding one or more service modules that may assist a user by providing services that are contextually relevant to content on a browser on the user's computer system. The agent also includes a data component for holding data objects related to and sharable by one or more service modules. The agent further includes a tracking component for tracking the pages on a browser and for communicating browser page data to a remote computer system that may respond to the agent with data or code for use by service modules. Contemplated service modules include a comparison shopping service, a transaction tracking service, and automated form filling service.

Abstract: A search engine can be configured to improve search times by implementing a parallel computing architecture. The index is split across a plurality of independent search nodes. A query is communicated to each of the parallel nodes and the query is searched in each independent node. The results from each node are routed to a federator that is configured to aggregate the results to a result set. The federator is configured to determine a subset of intermediate results to retrieve and aggregate from each of the independent nodes. The federator determines a number of results to retrieve from each of the nodes based at least in part on the number of nodes and the number of search results desired in the results set.

Abstract: A searcher can be configured to improve search results through the use of intelligent word deletion. A search auto categorizer (SAC) operates on the original query and returns a list of leaf categories and a distribution of probabilities among the leaf categories. The original query is parsed into word pairs and each word pair is run through the search engine. The search results for each word pair are weighted by the leaf category probabilities. A word pair is selected from the results and one of the two words is deleted from the original query. The searcher can perform exhaustive deletion where multiple truncated queries are generated from the original query and the results list from one truncated query is returned as the results list. The searcher can build up a truncated query from the original query by iteratively appending a word selected from the original query to the truncated query.

Abstract: A searcher can be configured to improve search results through the use of intelligent word deletion. A search auto categorizer (SAC) operates on the original query and returns a list of leaf categories and a distribution of probabilities among the leaf categories. The original query is parsed into word pairs and each word pair is run through the search engine. The search results for each word pair are weighted by the leaf category probabilities. A word pair is selected from the results and one of the two words is deleted from the original query. The searcher can perform exhaustive deletion where multiple truncated queries are generated from the original query and the results list from one truncated query is returned as the results list. The searcher can build up a truncated query from the original query by iteratively appending a word selected from the original query to the truncated query.

Abstract: A searcher can be configured to improve relevance ranking of search results through iterative weighting of search ranking results. A Search Auto Categorizer (SAC) operates on a base query to return a probabilistic distribution of leaf categories of a taxonomy in which relevant products may reside. A Search Logic Unit (SLU) can compute a relevance of any particular leaf category to the base query. The SLU can then determine an initial relevance of a particular product to the query based on the probabilistic distribution and the relevance of leaf category to query. The SLU weights the relevance of a product to the query to establish an updated probabilistic distribution. The SLU then repeats the relevance and weighting until convergence upon a relevance list.

Abstract: A computer implemented bidding method for use in bidding on auction items that allows for a desired optimization of bid amounts. The method includes selecting a set of one or more biddable items offered at auction wherein for a biddable item the auction offers a ranking for one or more received bids according to bid amounts, determining the current bids for a set of the biddable items, determining a bid amount for a selected biddable item based on a given utility function to determine a set of rankings for achieving a desired optimized outcome from the bidding, and submitting a set of bid amounts.

Abstract: The present invention relates to a system and method for data collection, evaluation, information generation and/or presentation. More particularly the present invention relates to a system for collecting, evaluating, and presenting data relating to electronic commerce. The system and methods of the present invention include one or more of the following: a module for stabilizing small or noisy samples of data, including the prediction of missing observations; alarm modules that alert an event handler when data values and/or rates cross specified thresholds; predictor modules that use recent historical data along with an estimated and/or available population function as the basis for a differential equation that defines the growth of the population to a saturation or maximum attainable level; and a dynamic icon that conveys to users of a system levels of predefined and/or observed activity occurring on the system.