9 results about "Search ranking" patented technology

Artificial intelligence (AI) techniques for connection networking are described. A method comprises generating a first training prompt based on a set of guidelines for a network service of a connection network system, the guidelines defining an objective for the network service, sending the first training prompt and a first set of training datapoints from a first training dataset to a first generative AI model, a training datapoint from the first set of training datapoints comprising a content item, receiving a second set of training datapoints for a second training dataset from the first generative AI model, wherein a training datapoint of the second training dataset comprises a first label for the content item generated by the first generative AI model based on the objective, and training a second generative AI model using the second set of training datapoints based on the objective. Other embodiments are described and claimed.

Embodiments of a method for automatic search engine optimization (SEO) of funnel websites in a tiered software framework comprises: generating at a first tier, a prompt for an artificial intelligence (AI) model to provide recommendations for improving search rankings of a seed website; receiving from the AI model, SEO format recommendations, and SEO content recommendations; generating websites by modifying the seed website according to a unique selection from the SEO format recommendations and the SEO content recommendations; associating each website with a corresponding SEO score based on performance in a web search; ranking and sorting the websites according to the respective SEO scores; generating choices of the ranked websites; providing the choices to a second tier and receiving a selection therefrom for a number of iterations; and deploying, by the funnel website application the final selection at a public universal resource locator.

A system and method for facilitating natural language conversations between customers and vendors for product purchases is provided. The system ingests product catalogs from vendors, normalizes the data, and provides a conversational interface for customers to query the catalogs. In some examples, a chatbot system receives a natural language query from a customer about a product in a chat interface, identifies vendors offering that product by searching uploaded product catalogs, determines available inventory for the product by querying the catalogs, and generates a natural language response to the customer using the vendor and inventory information. The system can extract product details from the query, search based on those details, rank and recommend vendors and products, update user profiles, offer purchase incentives, and complete transactions within the conversation. The system handles the conversational and technical aspects to enable natural dialog between businesses and customers regarding products.

This invention discloses a search ranking model generation method, ranking display method, apparatus, and device. The method includes: acquiring multiple historical input items entered by a user in a search box, a list of historical suggestion words corresponding to each historical input item, and historical search items determined by the user in the historical suggestion word list; forming positional bias-eliminating sample data based on the historical input items, the historical suggestion word list, and the historical search items; and training a deep learning model based on the positional bias-eliminating sample data, using a ranking loss function as the optimization objective, to obtain a search ranking model. This method, by focusing on positional bias information in the sample data, can generate a search ranking model that better conforms to relevance ranking, thereby facilitating the obtaining of suggestion word ranking results that better meet user needs through this search ranking model.

A technique for search ranking media content items is described. In accordance with the described techniques a media content item previously absent from a media content library is received. A popularity value of the media content item is estimated based on the artist popularity value and one or more album popularity values for one or more albums of the artist released prior to the media content item and based on an elapsed duration after receiving the media content item satisfying a threshold duration. A search request for a media content item is received. One or more search results are populated in response to the search request. The one or more search results include the media content item and one or more additional media content items ranked based on the estimated popularity value of the media content item and respective popularity values of the one or more additional media content items.

The application relates to the field of artificial intelligence, and provides a search sorting method and device, a server and a storage medium, the method comprising the following steps: when a content search request is acquired, acquiring a plurality of target network resources; acquiring a respective heat feature vector corresponding to each target network resource; running a preset click probability prediction model to process the respective heat feature vector corresponding to each target network resource, so as to obtain a prediction probability of user clicks on each target network resource; sorting the target network resources according to the prediction probability of user clicks on each target network resource, obtaining a search sorting result, and outputting the search sorting result. The application also relates to the field of blockchains, and the storage medium can store data created according to the use of a blockchain node. The method improves the accuracy of the search sorting result.

A computing system is provided including processing circuitry configured to implement a ranker module configured to jointly compute relevance scores of a set of candidate keywords to a query, and to generate a ranked list of candidate keywords based on the relevance scores. The ranker module includes a cross attention encoder configured to jointly compute the relevance scores by obtaining contextual embeddings for query tokens in the query and union of keyword tokens in the set of keywords, selectively pooling the contextual embeddings for the query tokens and the union of keyword tokens, inputting the selectively pooled contextual embeddings into a classifier that has been trained to predict a relevance score of the plurality of keywords to the query based on the contextual embeddings to output a relevance score for each query-keyword pair for the keywords in the set of candidate keywords, and outputting the ranked list of candidate keywords.

The present disclosure relates to systems and methods for improved searching. In some implementations, the approaches herein can be used to rank search results to provide more relevant results to users, for example by monitoring the interactions of one or more users with pages or other items. In some implementations, the approaches herein can be used to refine and improve the performance of a search result ranking model over time.

The application provides a search ranking method based on knowledge community discovery, which comprises the following steps: forming a knowledge community based on a domain knowledge graph in a domain to be searched; generating a topic and a description text for each knowledge community by using a large model; generating a semantic space vector according to the topic and the description text of each knowledge community; generating a semantic vector of a query word according to a user input query word, and recalling a candidate data set; retrieving n knowledge communities most similar to the query word as relevant knowledge communities from the candidate data set according to the semantic vector of the query word; selecting m nodes from the n relevant knowledge communities to obtain a node set; obtaining a knowledge-enhanced candidate document set by taking an intersection of the retrieved content and the node set; calculating the scores of the documents in the knowledge-enhanced candidate document set by using a knowledge-enhanced ranking method, and ranking the documents to complete the search ranking based on the knowledge community discovery.