37 results about "Social search" patented technology

System and methods for performing social searches using a social graph including nodes associated with voting values associated with items of interest. At least one rating (such as importance rating, compatibility rating, proximity rating, and / or social rank value) is obtained for each node. A web search obtains search results. A sort value (for sorting the search results) is determined for each search result in which an item is identified. The sort value may be determined for a search result by (a) identifying nodes associated with a voting value that is associated with an item identified in the search result, (b) determining a node value for each node identified based on the rating obtained for the node and the voting value associated with both the node and the item(s) identified in the search result, and (c) determining the sort value based on the node values of the nodes identified.

A computer-implemented method of management of queries for crowd searching is presented. In the method, an input model including input data e structured queries using query operators is mapped into an output model, which is obtained by modifying the input data and by adding the answers to the structured queries. Input data comprise at least one search object, which may be imported from a search system, at least one structured query operator corresponding to social interactions and a human-generated question related to the at least one search object. Mapping of the input model is according to a query task execution plan that defines a query task to be solved by a crowd, wherein mapping comprises selecting at least one social search engine running on a respective social platform and selecting one or more groups of responders interacting with the at least one social search engines and targeted to respond to the query task.

There is described a method implemented on a social search server for presenting names of members of a social network who shared information about a commercial item to a user performing a search from a user interface of a user device. The user is registered on the social network and has connections thereon. The method comprises: indexing data representative of activities of the members on the social network; extracting commercial items from the indexed activity data; receiving a search query along with a user identifier; identifying a commercial item within the search query; using the search query and the user identifier, performing a search on the extracted commercial items specific to the user's connections to obtain names of members who shared information about the commercial item; and returning the names of the members who shared information about the commercial item to the user device.

A method for generating correlations between human biological phenotype and human behavioral and / or emotional phenotype, and optionally to temporal location, comprising the steps of correlating data on biological phenotype with survey-based data on behavioral and / or emotional phenotype. The data on biological phenotype is collected from a sample from an individual, and the survey-based data can be collected from answers to behavioral and emotional questions from the individual or from observations of the individual by a third party. Correlations can further be used to predict behavior, including preferences, wellness needs and desires, and / or emotions. Feedback, advice and guidance can be provided to individuals based on such correlations. Such correlations are further useful for product and service providers and industries for purposes of standardizing or rating product quality and efficacy, and / or for promotion and selling purposes. A database comprising the data on biological phenotype and survey-based data is also provided.

Described is a new framework that combines the best of both exploratory search and social search: Collaborative exploratory search. This system allows a small group of focused information seekers to search through a collection of information in concert. The system provides exploratory feedback not only based on the individual's search behavior, but on the current, active search behavior of one's fellow search allies. The assumption is that the users who have gotten together to search collaboratively have the same information need, but differing perspectives and insights as to how to best express the queries to meet that need. The collaborative exploratory search system will therefore provide tools and algorithmic support to focus, enhance, and augment searcher activities. Searchers can, by interacting with each other through system-mediated information displays, help each other find all relevant information more efficiently and effectively.

The invention discloses a method of learning and optimizing a neural network based on a particle swarm optimization algorithm. The method comprises the steps: choosing macroscopic directional parameters of the particle swarm optimization algorithm and representing particle formation and swarm size; setting microscopic directional parameters of the particle swarm optimization algorithm and updating the own motions of particles according to social search and cognitive search; combining with the present parameters, training and learning with the particle swarm optimization algorithm and enabling the particles in the particle swarm to continuously approach an optimal particle; performing time estimation for rendering data by utilizing a trained neutral network model, and formulating swarm working and dispatching strategies to achieve a purpose of reducing actual rendering time. The method of learning and optimizing the neural network based on the particle swarm optimization algorithm is convenient to calculate, has rapid solution speed and is suitable for solving real numbers; and meanwhile, the easy convergence of the BP neural network to the locally optimal solution can be avoided.