53 results about "Search intent" patented technology

The invention discloses a lightweight display interface rendering optimization system, which relates to the technical field of image processing, and comprises an acquisition module, an interface analysis module, a behavior analysis module and an attention analysis and rendering module, dynamically calculating an effective view radius by utilizing a visual tunneling effect; the interface analysis module performs character string fuzzy matching in combination with the context input by the user, identifies the search intention of the user and improves the weight of a corresponding component; the attention analysis module performs multi-modal weighted fusion on the physiological fixation data and the sparse content saliency thermodynamic diagram; according to the method, by recognizing the semantic intention and the physiological fixation point of the user, on the premise that core visual experience continuity is guaranteed, GPU load and video memory bandwidth occupation are remarkably reduced, and balance of high-performance display and low-power-consumption operation is achieved.

The invention discloses a legal service intelligent searching and pushing system based on user requirements, particularly relates to the field of information processing, and is used for solving the problems of missing evidence binding and insufficient version anchoring in existing retrieval and touch. Executing structured positioning according to the retrieval intention to generate a fragment anchor identifier, querying a leatherwise table by the fragment anchor identifier to complete version coincidence judgment and paraphrasing closing judgment to generate an effective fragment pointing and rebounding entry, reading a touch node to bind and generate touch content, embedding the touch content into the rebounding entry, recovering a rebounding drop point, checking consistency and correcting a record, and performing rebounding. And a closed evidence chain of retrieval, judgment, reaching and checking is realized.

A user interface of an online system is generated based on search for relevant items that match ingredients of a recipe. After receiving, from a device associated with a user of the online system, a query for an ingredient of a recipe, the online system identifies, based on one or more attributes in the query, a set of candidate items for the ingredient. The online system generates a recipe relevance score for each candidate item by applying a weighted sum of scores, ranks the identified candidate items based on their recipe relevance scores, and selects one or more items for presentation to the user. The online system then generates a user interface of the device with a recipe page including the ingredient of the recipe and the one or more items that match the ingredient of the recipe.

The invention discloses a personalized search method and system for an electric power knowledge base, and relates to the technical field of electric power informatization and intelligent search, and the method comprises the steps: generating a user interest vector representing the current search intention of a user through a weight adaptive algorithm, and constructing a user interest model for the electric power field; receiving a query request input by a user, and retrieving from the power knowledge base based on the query request to obtain a corresponding initial search result set; constructing a queried power field polysemy model based on formal concept analysis (FCA); calculating the similarity between the interest vector and each semantic category based on the user interest vector; reordering the documents in the initial search result set according to the similarity; and selecting a feature word from the semantic category with the highest similarity as an expansion word. According to the method, through user interest modeling and semantic category matching, accurate sorting of the power knowledge base results is achieved, the retrieval individuation degree is improved, and the search accuracy and the user experience are improved.

The embodiment of the invention discloses a search method and electronic equipment, in the method, the electronic equipment can identify the search intention of a user, implement priority search based on the search intention of the user, and preferentially present a target search result meeting the search intention of the user, so that the waiting time of the user for obtaining the target search result can be obviously shortened, and the user experience is improved. The user can conveniently notice the target search result at a glance, and the attention burden of the user is relieved. Moreover, the search triggering and the search intention transmission can be combined into one on the user operation, and the user does not need to separately implement the user operation for triggering the search and the user operation for transmitting the search intention, so that the user operation is simplified, and the machine using efficiency is improved.

The invention provides a man-machine interaction method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining multi-modal data of a current interaction round, the multi-modal data at least comprising an input image and an input text; based on the input image, performing image recognition by applying a multi-modal large language model to obtain image information corresponding to the input image; based on the input text, performing intention recognition by applying a multi-modal large language model to obtain a retrieval intention corresponding to the input text; based on the image information, the retrieval intention and a historical commodity retrieval result corresponding to the historical interaction round, performing decision analysis by applying a multi-modal large language model, and determining a retrieval strategy; the retrieval strategy comprises a joint knowledge base retrieval strategy, a historical retrieval result reuse strategy or a non-retrieval strategy; and obtaining reply information based on the retrieval strategy. Differentiated retrieval is performed according to actual retrieval requirements, so that consumption of invalid resources is reduced, and retrieval efficiency is improved.

The invention provides a natural language-based search method and system, and the method comprises the steps: obtaining an input natural language demand, and obtaining an input demand; an intention recognition agent is adopted to conduct intention analysis on the input requirement to obtain a search intention, the intention recognition agent is an agent obtained through pre-training and used for understanding a natural language, and the search intention is a target expected to be achieved by the target object; and performing retrieval according to the search intention to obtain retrieval data, and sending the retrieval data to target equipment used by the target object, so that the target object processes the business by using the retrieval data. According to the method, the problem that in the prior art, a bank APP search function mainly depends on a keyword matching technology, and the search accuracy is low is solved.

The data retrieval method comprises the following steps: performing OCR (Optical Character Recognition) and paragraph splitting on an original document to generate a parent segmentation abstract and a child segmentation abstract which have a hierarchical relationship, and fusing an abstract text with metadata to obtain an abstract file; constructing a structured data abstract directory containing semantic features and attribute features; obtaining user dialogue history and current input, constructing a semantic input matrix, and performing semantic analysis and intention reasoning under role and task constraints by using a large model to generate a standardized search intention; inputting the standardized search intention and the structured data abstract directory into a large model, screening candidate abstracts through a bidirectional association matching mechanism, performing hierarchical mapping based on a knowledge base index rule, and reasoning to generate an original storage index of target data; and carrying out validity verification on the original storage index generated by reasoning, obtaining original data content according to the index passing the verification, and returning the original data content.

The embodiment of the invention discloses a live broadcast interaction method and device, equipment, a storage medium and a program product. The method comprises the steps of displaying a search result page in response to a search operation based on target search information; at least one e-commerce live broadcast included in the search result page is a live broadcast of a live broadcast commodity including a target commodity in the live broadcast shopping cart, and the target commodity is determined according to the target search information; in response to a selection operation on a target e-commerce live broadcast in the at least one e-commerce live broadcast, determining a target commodity matched with the target search information in a live broadcast shopping cart of the target e-commerce live broadcast, displaying a live broadcast interface of the target e-commerce live broadcast, and displaying a commodity card in the live broadcast interface of the target e-commerce live broadcast, the commodity card is used for trading the determined target commodity. By adopting the embodiment of the invention, the search intention of the user can be accurately identified, so that the user can realize seamless switching between search and live broadcast transaction, and thus the live broadcast interaction efficiency is improved.

Alternative options are presented to users at the point of personalization of a search engine to generate personalized query suggestions. Seed items within a module carousel comprising item cards on a homepage are identified. Embeddings are retrieved for at least a portion of the seed items. Based on the embeddings, related item embeddings corresponding to related items that were purchased by other users are identified. A list of queries for each of the related items that were purchased by the other users is retrieved. A ranking for each query of the list of queries is generated. Based on the ranking, queries of the list of queries are presented in place of at least one of the item cards within the module carousel. In various aspects, the queries of the list of queries broaden the intent, narrow the intent, or diversify the intent.

This invention belongs to the field of computer information processing and proposes a document retrieval optimization method based on entity association. It performs secondary optimization while retaining the original retrieval algorithm, which not only improves the accuracy of the retrieval but also avoids the accuracy and efficiency problems caused by algorithm modifications. By performing entity recognition on both the document set and the user's search content, and calculating the association relationships between entities and documents, and entities and search content respectively, and comprehensively applying these association coefficients, it can uncover deep semantic relationships between search content and documents, and accurately locate documents that match the search intent. By calculating entity association degrees and re-ranking them according to these degrees, the accuracy of the search results is improved, resulting in a higher degree of alignment with user needs and allowing users to more easily obtain the information they require.

This application provides a method, electronic device, and storage medium for identifying boiler weld defects. The method includes: acquiring a radiographic digital image of a sample to be evaluated and corresponding metadata; obtaining a first parameter set based on a preset defect detection model; obtaining a second parameter set and search intent for each suspected defect based on the first parameter set and metadata, using a pre-trained large language model; inputting the search intent into an industry standard database and a historical case database to obtain standard evidence and historical case evidence; determining an initial defect based on the first parameter set, the second parameter set, the standard evidence, and the historical case evidence; obtaining a consistency score by combining the first parameter set, the second parameter set, the standard evidence, and the historical case evidence; and correcting the initial defect using the consistency score to obtain the boiler weld defect identification result. This application improves the accuracy and reliability of defect identification results.

The invention discloses a file searching method, device and equipment, a medium and a product, and belongs to the technical field of electronic equipment. The file search method comprises the steps that in response to a search intention of a user, a first interface is displayed, the first interface comprises metadata, the metadata comprises a virtual file handle and a file name of a candidate file, and the candidate file is associated with the search intention; receiving a first input to target metadata, wherein the target metadata is at least one of the metadata; and in response to the first input, displaying the file content of the target file corresponding to the target metadata.

The invention relates to the technical field of intelligent search, and discloses an intelligent agent-based e-commerce product search method and device, a medium and a program product, the method comprises the following steps: receiving a current round of search input of a user, and analyzing and constructing a search condition; comparing historical search conditions, collecting change characteristics, and judging the stable state of the search intention; calling a search engine to output a commodity result, collecting user interaction behaviors, and judging a trust state of a search result; and adjusting a subsequent search condition construction strategy and / or a commodity sorting strategy according to the combination linkage of the two, and finally outputting a commodity search result. According to the method, the stability of the search intention and the credibility of the result are jointly evaluated and dynamically adjusted, negative circulation formed by the stability and the credibility of the search intention is broken, repeated search and invalid recommendation are reduced, the matching accuracy of the search result and the credibility of the user are improved, and e-commerce search experience and conversion efficiency are optimized.

The invention provides an information retrieval method and system based on large model deep search and real-time semantic analysis, and relates to the technical field of information retrieval and artificial intelligence. The method comprises the steps that according to a domain semantic association database, a local multi-level association keyword library is constructed and dynamically maintained; receiving a user search word, performing intention analysis on the user search word through the large model to generate user search intention information, and performing initial retrieval according to the user search intention information to generate initial retrieval result information; performing real-time semantic verification on the preliminary retrieval result information, and screening out target preliminary retrieval result information or no matching result identifier; if no matching result identification is generated, matching the user search word with a local multi-level associated keyword library, and screening out a high-association-degree expansion word; executing secondary retrieval to generate target secondary retrieval result information; and combining and sorting the target secondary retrieval result information and the target preliminary retrieval result information, and then outputting.

This application discloses a search method and related apparatus. The processing device can first identify the text type corresponding to the search text, and then use different category intent recognition methods for different text types to determine the category corresponding to the search text. Finally, based on the category, it returns the target object corresponding to the search text. Therefore, on the one hand, the category intent recognition method can better match the text features, resulting in more accurate category recognition results; on the other hand, it can overcome the limitations of text relevance, obtaining more comprehensive search results, and ultimately providing users with target objects that better match their actual search intent. This is particularly beneficial in e-commerce scenarios where the text relevance between object information and search text is often poor, significantly improving the user's search experience. This application relates to artificial intelligence technology and can be applied to related scenarios of in-vehicle IoT.

The invention provides a user intention mining method and system based on a double-tower model, a program and a storage medium. The system comprises a data acquisition device, a text graph enhanced representation device, a model training device and an intention mining device. The method comprises the following steps: firstly, mining and converting a user search intention into a dense retrieval problem, and realizing formalized definition of the user search intention and a dialogue text through a Sension-BERT model; then constructing a retrieval architecture based on a double-tower model, performing feature enhancement in combination with a text graph neural network, and capturing text deep association through a semantic graph, a syntactic graph and a sequence graph; meanwhile, an interval constraint negative sample sampling strategy is put forward, effective negative samples are mined from front and back, and the model is trained through a noise comparison estimation method. The method can be deployed at the rear end of each server room, and can widely provide efficient user intention mining support for decision-making systems such as an intelligent medical auxiliary system and the like.

The invention discloses a commodity search intention enhancement method based on a multi-modal model, and relates to the technical field of artificial intelligence and Internet information processing, a query intention vector is input to a generative intention extension module, the generative intention extension module is constructed based on a pre-trained generative language model, and the query intention vector is input to a query intention enhancement module; and the intention extension module is used for generating a potential intention keyword and a corresponding intention weight according to the query intention vector, and carrying out weighted enhancement on an original query semantic vector by utilizing the potential intention keyword to obtain an enhanced search intention vector. According to fuzzy input and historical visual preferences of a user, brain tonifying can be actively carried out, and potential intention keywords (such as shoe + historical click red-gt) of the user are generated; the wedding shoes and the festival are generated, and the intention understanding depth is remarkably improved.

The invention discloses a multi-omics search engine for comprehensive analysis of cancer genomes and clinical data, which is implemented by a computer system and comprises a processor, a memory and program instructions stored in the memory and executable by the processor, the program instruction is configured to enable the processor to execute the following steps: constructing and maintaining a dynamic multi-omics semantic index structure, and executing a closed-loop index optimization process in a process of continuously receiving and responding to a search request; the search target type, the used target feature subset, the local structure reconfiguration operation and the corresponding search result of each search request are recorded, the influence of the search on the search effectiveness is analyzed, and the analysis result serves as optimization feedback; and based on optimization feedback, driving the multi-omics semantic index structure to perform self-growing update. According to the method, the limitation of a static index structure on the search intention adaptation capability can be broken through, and the index structure can be dynamically adjusted according to a specific search target.

Systems and methods for progressing search instances in weak signal instances can include obtaining a visual query, determining a plurality of initial search results, determining the visual query has weak search signals based on the plurality of initial search results or search intent ambiguity, generating a model-generated output to prompt the user, and providing the model-generated output for display with a subset of search results. The model-generated output can include a model-generated prompt that provides an interface for a user to provide additional inputs for additional search query clarity.

The application discloses a medical field-oriented intelligent search system which is composed of a data management module, a data processing module, a natural language processing service module, a natural language understanding module, an information retrieval module and a result display module. The application realizes a search function based on natural language understanding by applying and researching Solr and NLU models and processing medical data by using a natural language processing technology. The search function has distinct intention characteristics of a patient's question sentence, which is the reason for selecting medical data. The NLU model data is trained by a large amount of medical data marking, so that the NLU model data has excellent intention classification characteristics in different departments and different types of diseases. To some extent, the application enables a search engine to understand a user's search intention more and makes the search engine more intelligent.

The application realizes a commodity search method with query-related multi-aspect explainability by means of a method in the field of artificial intelligence. The search information of an input commodity is searched and the query commodity result is fed back by using a multi-path query perception graph convolution network for explainable commodity search. The multi-path query perception graph convolution network for explainable commodity search comprises two components: a query perception graph convolution sorter and a query perception multi-path reasoner. The query perception graph convolution sorter models the representation of a user and a commodity according to different knowledge relationship domains in a knowledge graph by using a graph convolution network. The query perception multi-path reasoner is responsible for exploring a query-specific multi-path from the knowledge graph to meet the search intention of the user. The two components share basic parameters and are collaboratively trained to constitute a complete network. The method can significantly improve the retrieval performance and generate better explanations for the search result.

The invention provides a multi-agent-based search fault sample labeling method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, in particular to the fields of large language models, agents, data search and the like. According to the implementation scheme, sorting feature data and user behavior data are obtained, and the sorting feature data and the user behavior data respectively comprise prediction result data obtained by sorting search results through a search sorting model and feedback data of a user on the prediction result data; calling a sample inspection agent, determining a search fault sample based on the sorting feature data and the user behavior data, and representing that the prediction result data is not consistent with the search intention of the user; calling a sample analysis agent, determining the fault type of a search fault sample, and indicating the reason that the prediction result data is not consistent with the search intention; and calling a sample labeling agent to label the search fault sample so as to generate a training sample for training the search sorting model.

The invention relates to a training method of a search matching model. The method comprises the steps of obtaining a training data set, wherein the training data set comprises an information two-tuple of a target search word and an information object; the target search word in each information two-tuple corresponds to an information inquiry intention, and the information object corresponds to an information providing intention; based on each information two-tuple in the training data set, obtaining a pre-trained twin-tower model; fusing the target search word and the information inquiry intention corresponding to the target search word into a search intention word, and fusing the information object and the information providing intention corresponding to the information object into an information carrier word; performing fine tuning training on the pre-trained double-tower model based on the search intention word and the information carrier word to obtain a trained double-tower model; the trained double-tower model is used for object retrieval. By adopting the method, the accuracy of object retrieval is improved.

The embodiment of the invention discloses a live broadcast interaction method and device, equipment, a storage medium and a program product. The method comprises the steps of displaying a search result page in response to a search operation based on target search information; the search result page comprises preview content of at least one e-commerce live broadcast and a first commodity card of a target commodity matched with the target search information in a live broadcast shopping cart of each e-commerce live broadcast; and in response to a selection operation on a target e-commerce live broadcast in the at least one e-commerce live broadcast, displaying a live broadcast interface of the target e-commerce live broadcast, and displaying a second commodity card in the live broadcast interface of the target e-commerce live broadcast, the second commodity card being used for trading a target commodity of the target e-commerce live broadcast, the second commodity card is triggered and displayed based on the search operation. By adopting the embodiment of the invention, the search intention of the user can be accurately identified, so that the user can realize seamless switching between search and live broadcast transaction, and thus the live broadcast interaction efficiency is improved.

The invention relates to the technical field of artificial intelligence, can be applied to the medical field and the financial field, and discloses an intelligent pushing method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a call voice signal, and converting the call voice signal into text data in a streaming recognition mode; performing multi-label intention recognition on the text data to obtain an intention recognition result, and generating a semantic triple based on the intention recognition result; judging that the client has a new retrieval intention according to the intention recognition result, and when judging that the client has the new retrieval intention, generating a structured query question based on the semantic triple; based on the structured query question, retrieving a preset knowledge base through a mixed retrieval mechanism to obtain at least one candidate knowledge fragment; performing knowledge fusion and noise filtering processing on the candidate knowledge fragment to obtain a target knowledge fragment; and carrying out multi-modal intelligent pushing on the target knowledge fragment. The knowledge service capability of the customer service industry is remarkably improved.

Provided is an image-based search processing method, an electronic device and a storage medium, relating to the technical fields of computer vision, deep learning, natural language processing, and searching. The method includes: determining, in response to receiving an image sent by a terminal, a first requirement corresponding to the image, the first requirement including an original search intent of a user; generating a search result and a recommended content corresponding to the image according to the first requirement in a case where the first requirement meets a recommendation triggering condition; and returning the search result and the recommended content to the terminal to cause the terminal to output the search result and the recommended content on a search result page.

This disclosure relates to search recommendation, multi-agent collaboration, and mapping, particularly to a hotel recommendation method, device, electronic device, and storage medium based on multi-agent collaboration. The specific implementation involves: a first agent recalling candidate hotels based on acquired user profile information, environmental information, and user search intent; a second agent evaluating the candidate hotel set, and if the set does not meet user needs, sending feedback to the first agent, triggering the first agent to regenerate the candidate hotel set until it meets user needs; and a third agent ranking the candidate hotel set to generate a recommendation result. This disclosure continuously optimizes the recommendation result through the interaction and feedback mechanism between agents, automatically generating recommendation results that meet user personalized preferences and scenario requirements without manual rule intervention.

A user can submit a visual query that includes one or more images. Within or in connection with the visual query, various processing techniques, such as optical character recognition (OCR) techniques, can be used to recognize text (e.g., in the image(s), around the image(s), etc.) and / or various object detection techniques (e.g., machine learning object detection models, etc.) can be used to detect objects (e.g., products, landmarks, animals, humans, etc.). Content related to the detected text or object(s) can be identified and potentially provided to the user as search results or proactive content feeds. Thus, various aspects of the present disclosure enable visual search systems to more intelligently process visual queries to provide improved search results and content feeds, including those that are more personalized and / or take into account implicit features of the visual query and / or user search intent.

The application relates to a content search method and device, computer equipment, a storage medium and a product. The method comprises the following steps: obtaining a first search result corresponding to search content; the first search result is obtained in response to a search request comprising the search content, and comprises target content generated for the search content and published content related to the search content; the target content is generated based on a search intention determined based on the search content and logic corresponding to the search intention; based on the first search result, a search result page is displayed; wherein the search result page comprises a first result area and a second result area, the first result area displays the target content generated for the search content, and the second result area displays the published content related to the search content. By displaying the target content generated for the search content in the search result page, an interactive function with higher interactivity can be provided for a content search scene, and the interactivity and interest of content search are improved.