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 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: 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: 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 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 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.