Abstract: A computer-implemented system is constructed and adapted to: associate at least one user with a subscriber to a system; obtain and store at least one content contribution from said at least one user; determine relevant content from said at least one content contribution; determine a booklet layout for said relevant content; and produce said booklet with said relevant content based on booklet layout.

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: 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 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: Methods and systems for location-based promotions are presented. A location of a mobile device is determined and transmitted to a server computer via a wireless communications network, along with user identification information. One or more location-based applications are run on the mobile device. At least one of the applications receives location-based content based on a context determined by the server computer. The location-based content including a promotion for a location associated with the location-based content. The application further executes a promotional function based on the promotion.

Abstract: Systems and methods for location discovery are presented. One or more applications are run on the mobile device. At least one of the applications receives, from a server computer via the wireless communications network, location-based content based on a context determined by the server computer, the location-based content including an a location-based application associated with a location of the mobile device, the context being based on the location of the mobile device and a profile of a user of the mobile device. The application accesses the location-based content to execute the location-based application to generate a function that is personalized for the user of the mobile device based on the context, and executes the function based an input from the user of the mobile device.

Abstract: A system and method for determining a relevance of content for delivery to a mobile device is presented. Location information about a location of the mobile device is received. A set of location-based content related to the location is determined based on the location information and a user affinity of a user associated with the mobile device. The set of location-based content describes one or more places of interest to the user. A distance between each of the one or more places of interest and the mobile device is computed according to the location of the mobile device. A final relevance value for each item of content in the set of location-based content is determined, and the content from the set of location-based content is displayed according to the content's final relevance value.

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.