40 results about "Personalized search" patented technology

The invention relates to the field of distributed information retrieval, and particularly discloses a memory architecture vector approximate retrieval method based on a graph neural network, which comprises the following steps of: constructing and dynamically maintaining a historical query-hit vector association graph for modeling a deep semantic relationship between a historical query and a successful retrieval result; inputting the features of the current query vector, the information of the current query vector subjected to neighborhood sampling and feature aggregation in the graph and the service scene label into a lightweight graph neural network, and predicting the approximate retrieval tolerance level of the query; on the basis of the prediction result, an optimal retrieval strategy is generated in a self-adaptive mode; and in combination with asynchronous result return and a progressive refinement mechanism based on residual error reordering, a user is responded at the first time, and continuous optimization and pushing of a better result are realized. According to the method, a query-level personalized retrieval strategy is realized, the retrieval precision, the response delay and the system resource consumption are effectively balanced, and the method is suitable for a large-scale high-dimensional vector retrieval scene.

An information search method, apparatus, and computer-readable storage medium for providing intelligent search recommendations based on content marking behavior. The method displays a content detail interface and receives marking operations selecting content segments within target content. Marking identifiers are displayed for the selected segments. Based on segment detail information from current and previous marking operations, recommended search information is generated and displayed. Users can select target recommended search information through search operations, and corresponding search results are displayed. This approach enables personalized search suggestions derived from user marking patterns and content interaction history, improving search relevance and user experience.

This invention relates to a personalized search and recommendation method for smart TVs, specifically in the field of film and television recommendation. It utilizes a deep neural network model to generate embedding feature vectors for each film and television program in a media resource library. User profiles are obtained based on MAC addresses or voiceprint IDs, and embedding feature vectors for user-preferred films and television programs are generated using the same deep neural network model. A film and television knowledge graph is constructed, with the relationships between knowledge graph nodes serving as the reasoning for recommendations to the user. The method obtains the first- and second-degree neighbor film and television IDs for any given film and television program. When a user inputs a search term, the first- and second-degree neighbor film and television IDs are obtained using the corresponding film and television ID. The distance between the embedding feature vector of each film and television ID in the first- and second-degree neighbor programs and the embedding feature vector of the user-preferred films and television programs is calculated. These distances are then sorted from smallest to largest, and the top N films and television programs are selected for recommendation to the user. This invention solves the problem of weak correlation between search recommendation results and specific user preferences in existing technologies. This invention is applicable to personalized film and television recommendation.

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 application realizes a personalized search and explanation generation method based on unified prompt perception through a method in the field of artificial intelligence. The user, the submitted query and the user's behavior sequence are taken as input, the unified personalized product search and explanation generation prompt perception framework is input, the search task and the explanation task are output, and the unified personalized product search and explanation generation prompt perception framework is composed of three parts: a base pre-training language model, a personalized retrieval component and an explanation generation component. The application proposes a unified training framework for personalized product search and explanation generation based on prompt perception, and designs specific prompts for each task in a unified manner, and the model finally outputs a natural language form of explanation while outputting the target product.

In one embodiment, a computer implemented method for generating personalized search results is disclosed. The method may include processing, via a processor, a search query of a user, modifying, via the processor, the search query based on educational competency data of the user, generating, via the processor, a search result based on the modified search query by performing a search operation in a knowledge space based on the modified search query to retrieve one or more content items of the knowledge space corresponding to the modified search query, generating, via the processor, a user interface configured to display the search result; and transmitting, via the processor, the user interface to a user device associated with the user.

The invention relates to the technical field of machine learning, in particular to a learning content pushing method, system and device and a readable storage medium, and the method comprises the steps: obtaining a multi-modal data source, and constructing a hierarchical index knowledge graph through cross-modal semantic feature extraction; collecting user behavior data in real time, and generating a knowledge state feature sequence; dynamically aligning the query vector with the knowledge graph through a graph structure alignment calculation module, calculating and aggregating according to semantic similarity, and generating a personalized retrieval weight; dynamically adjusting a graph query path according to the weight, and retrieving and pushing a matched knowledge set; and finally, according to the knowledge structured reconstruction degree and the data feedback of mastering the attenuation value, a query path optimization algorithm is adopted, and the graph index is updated online. The problem that an existing information retrieval system is insufficient in unstructured data processing capacity and cannot dynamically adapt to query intentions is solved, and the accuracy of data retrieval is improved.

Systems and methods for generating retrieval queries for a user. Such a method includes (i) receiving a search query from a user associated with an account profile, the account profile including a user history; (ii) generating, based on the search query, a context attribute summary indicative of at least one of (a) one or more content items of interest for the user or (b) predictions for future searches by the user; (iii) generating, based on the user history, a summarized user browsing history indicative of one or more past searches performed by the user; (iv) generating, using the context attribute summary and the summarized user browsing history as inputs to a trained language model, a plurality of retrieval queries; and (v) transmitting the plurality of retrieval queries to a search model to generate one or more recommendations associated with the search query for the user.

Disclosed are system, method and / or computer program product embodiments that retrieve items for a user based on a query using a two-tower deep machine learning model. An example embodiment provides input to a context tower, wherein the input includes the query and one or more of a query embedding corresponding to the query or a graph user embedding corresponding to the user. The context tower generates a context embedding in a vector space based on the input. The model determines a measure of similarity between the context embedding and each of a plurality of item embeddings in the vector space that are generated by an item tower and represent a plurality of candidate items. A relevancy score is calculated for each candidate item based on the measure of similarity between the context embedding and the corresponding item embedding. The relevancy scores are used for item retrieval and / or ranking.

The invention provides an AI-based search method, an AI-based search device, a computer readable storage medium and electronic equipment, and is applied to the technical field of artificial intelligence. The method comprises the steps that according to vector representation of a search term and vector representation of a structured abstract of an ith article, the ith search term association degree between the search term and the ith article is determined, and i is a positive integer; calculating a correlation degree between portrait information of a target user corresponding to the search word and the ith article to obtain an ith personalized correlation degree; according to the ith search word association degree and the ith personalized association degree, determining a score about the ith article; and sorting the articles according to the scores corresponding to the articles to obtain a search result. According to the method and the device, personalized search experience can be provided for each search user while the search accuracy is improved.

The invention relates to the technical field of intelligent medical treatment, in particular to an ankle joint exoskeleton auxiliary strategy generation method and device based on personalized search. The method comprises three core technologies of accurate gait phase detection based on IMU, personalized search space prediction based on ATLSTM, and real-time gait track generation based on PVT curve. The problems that in existing ankle joint exoskeleton personalized auxiliary strategy generation, a model-based method is high in complexity, a learning-based method needs a large amount of data and lacks real-time feedback, and human loop optimization causes slow convergence or local optimal pain points due to individual differences are effectively solved. According to the method, a complex human body dynamic model does not need to be constructed, data dependence and calculation cost are reduced, the loop optimization convergence speed of people can be remarkably increased by locking a personalized search space, a smooth dorsiflexion assisting track fitting an individual gait is generated, and man-machine collaboration, practicability and scene adaptability of exoskeleton are greatly improved.

Disclosed is a search system and associated methods for proactively searching disparate data sources in order to track in real-time the continuously changing attributes that are searchable and to generate personalized search results for different users based on machine-generated queries of relevant searchable attributes to each user without user-defined queries or user-initiated searches. The search system continuously searches the data sources for changes to a first set of searchable attributes with relevance to a first user and to a second set of searchable attributes to a second user. The search system generates and performs a first customized query of the first set of attributes identified with the changes and a second customized query of the second set of attributes identified with the changes, and provides the personalized search results from each query to the respective user in response to detecting that respective user accessing the search system.

The invention discloses a multi-unmanned aerial vehicle cooperative task allocation method with a self-evolution search capability, belongs to the technical field of emergency rescue, and aims to solve the problems that the multi-unmanned aerial vehicle cooperative task allocation method is easy to fall into local optimum and is weak in constraint processing capability. Comprising the steps that task modeling and parameter initialization are carried out, and an emergency rescue task is converted into a computable mathematical model; decoding the particles and executing deadlock detection and repair, and converting continuous solution vectors in particle swarm optimization into executable task allocation schemes meeting all rescue scene constraints; calculating a fitness value and a self-adaptive inertia weight of each particle to realize personalized search for different particles; convergence stagnation is monitored, intelligent variable neighborhood search and disturbance are triggered, and double-adaptive capacity of depth and neighborhood selection is achieved; updating the particles by using a self-adaptive inertia weight; and repeating the steps until the termination condition is met. According to the method, the solving quality and stability of a multi-unmanned aerial vehicle cooperative task allocation scheme under complex constraints can be remarkably improved.

This disclosure relates to a speech recognition method, apparatus, device, vehicle, and medium. The method includes: performing speech recognition on voice navigation information to obtain at least one candidate recognition result; determining an acoustic score for each candidate recognition result and a language score for each candidate recognition result; identifying target hot words contained in each candidate recognition result according to a preset hot word library and obtaining a target hot word score for the predetermined target hot words; determining a reference score for each candidate recognition result based on the acoustic score, language score, and target hot word score, and determining the target recognition result based on the reference score. In the embodiments of this disclosure, based on the speech recognition results combining language and acoustics, client-side hot words are introduced to determine the final recognition result. Since client-side hot words reflect personalized search habits, the influence of similar sounds can be removed, further improving the accuracy of the recognition results.

The application discloses a search recommendation method, device and equipment. The method receives a search request of a user, the search request carrying a user identifier and a search word; determines whether a historical user behavior corresponding to the user identifier is related to the search word according to the search request; if there is no historical user behavior, or there is a historical user behavior but the historical user behavior is not related to the search word, a user group corresponding to the user identifier is determined; and a recommendation result corresponding to the search word is determined according to the user group. In this way, the commodity interaction behavior of the user group to which the low-activity user (including a new user) belongs and which is related to the search is used to recall the commodity that the low-activity user who has no or few commodity interaction behaviors related to the search likes, so that a personalized search recommendation mode for a general group is realized, and therefore, the conversion efficiency and the user experience can be improved.

The invention discloses a personalized retrieval type clothing recommendation method and system based on historical data perception. The method comprises the following steps: 1, acquiring historical clothing data and single-item multi-modal information of a user; 2, extracting image and text features through multi-modal information of a CLIP encoder, and fusing the image and text features; 3, performing feature extraction and fusion on the single-item image and the text description in the step 1 to generate a single-item multi-modal representation vector; 4, inputting the multi-modal representation vector of each single item in the garment sequence to be evaluated into a Transform encoder, and outputting a compatibility embedded vector representing overall matching; 5, constructing a joint loss function, and optimizing compatibility prediction and personalized recommendation targets; 6, balancing compatibility and personalization by adopting two-stage training; and 7, performing answer prediction based on the distance between the compatibility embedding vectors to calculate the accuracy, and calculating a personalized matching score based on the correlation between the compatibility embedding vector of the predicted answer and the historical latent variable of the user.

The invention relates to an archive retrieval result dynamic optimization method based on reinforcement learning, and belongs to the field of computer software and artificial intelligence. According to the method, archive data are subjected to retrieval batch preprocessing to obtain related archives, the related archives and input retrieval words are synchronously sent into a retrieval model for encoding, the retrieval model is mainly composed of a gradient learning structure and a space-time fusion network, and the retrieval process is iteratively optimized through a retrieval optimization method based on reinforcement learning; and designing an embedded Rocchio algorithm to synchronously update code query, and finally outputting a retrieval result. According to the method, the retrieval batch processing efficiency is improved, the data value is fully mined, the candidate file selection logic is optimized, invalid retrieval results are reduced, personalized retrieval optimization is achieved, and the dynamic requirements of users are met.

The invention discloses a method and a system for constructing personalized search entries of cultural and creative products based on visual semantic understanding, and relates to the technical field of internet data processing and computer vision. The method comprises the following steps: firstly, carrying out hierarchical scanning on a cultural and creative object by utilizing visual acquisition equipment to obtain a macroscopic contour and a microscopic texture; thirdly, identifying the type of a physical carrier based on the macroscopic contour, constructing a reverse mask, forcibly rejecting the characteristics of the physical carrier by using Boolean operation, and extracting a cultural visual semantic vector independent of the carrier from the reserved region; meanwhile, establishing a screen interaction coordinate system, and monitoring a fixation point track and a touch zooming parameter of the user; then, calculating a derivative search desire degree of the user for the current visual semantics, and when the desire degree exceeds a threshold value, generating a matched virtual derivative thumbnail and rendering the virtual derivative thumbnail as a dynamic entrance; according to the method, the problem of'carrier-semantic 'conflict in creative search is effectively solved, and accurate intention pre-judgment and cross-morphological search based on user subconsciousness behaviors are realized.

The invention belongs to the technical field of artificial intelligence, and relates to a search keyword generation method, which comprises the following steps: constructing a prompt text and establishing a scoring rule set according to user basic information and historical behavior data, and constructing a fine tuning training sample set based on the prompt text and the scoring rule set, inputting the fine tuning training sample set into a pre-trained large language model to generate a candidate search keyword set, scoring the candidate search keyword set by adopting a scoring rule set to obtain a keyword scoring result, and performing fine tuning on the large language model through a preset reinforcement learning algorithm until an iteration stop condition is met, a final keyword generation model is obtained, and personalized search keywords are generated according to the target prompt text through the keyword generation model. The invention further provides a search keyword generation device, computer equipment and a storage medium. The keyword searching method and device can be applied to business management program systems such as financial science and technology, it is guaranteed that the generated keywords have high adaptability, and the keyword searching accuracy is improved.

The invention discloses a personalized film and television retrieval method which comprises the following steps: acquiring long-term behavior data of a user, and generating a long-term preference vector through a neural network model based on the long-term behavior data; collecting behavior sequence data of a user in a current session in real time, and generating a real-time intention vector through a time sequence model based on the behavior sequence data; receiving a current query input by a user, and converting the current query into a query vector; dynamically calculating a weight distribution proportion of the long-term preference vector and the real-time intention vector according to the semantic context of the current query; on the basis of the weight distribution proportion, the long-term preference vector and the real-time intention vector are aligned and then weighted fusion is carried out, and a user state vector is generated; fusing the user state vector with the query vector to generate an enhanced query vector; performing similarity retrieval in the film and television content vector library by utilizing the enhanced query vector, and returning a personalized retrieval result list; according to the method, the movie and television retrieval accuracy and the user satisfaction are remarkably improved.

A computer server system and method are disclosed for personalization and customization of search results and search result rankings, such as for Internet searching. A representative server system comprises: a network interface to receive a primary query and transmit a plurality of secondary queries and search results; a data storage device storing a vector database; and one or more processors configured to generate the secondary queries, using the primary query and using responses received to prior secondary queries; to select and concatenate responses to the secondary queries; using a vector formed from the response concatenation, to perform a vector search of the vector database and obtain associated classifications or categories; to filter and rank the associated classifications or categories to generate and output personalized search results and search result rankings comprising one or more associated classifications or categories corresponding to the primary query.

Described are systems and method for personalized search results, including a memory storing instructions, a trained machine learning model, and a processor operatively connected to the memory and configured to execute the instructions to perform operations, including receiving the sequence of search queries from a user device associated with a user, predicting the likely next search query from the user by inputting the received sequence of search queries into the trained machine learning model, generating predicted search results by applying the likely next search query, generating the personalized search results by appending the predicted search results to search results from a most recent query of the sequence of queries from the user, and causing the user device to display the personalized search results.

The invention belongs to the technical field of document retrieval, and provides an intelligent retrieval method and system for online document management. The method comprises the following steps of: dividing a document, and establishing an independent index for each document block in a search engine by taking the document block as a minimum index unit; the method comprises the following steps: receiving a search request input by a user, performing authority verification on the search request, then performing natural language processing and vectorization coding, performing keyword inverted index retrieval and vector semantic retrieval after to-be-retrieved content is obtained, and performing result fusion to generate a result sorting list; dynamically adjusting the result sorting list on the basis of user behavior characteristics, highlighting contents matched with a user search request, and automatically recommending similar documents when the user does not search a target document; and monitoring the state of the document in real time, and updating the dynamic incremental index. According to the method and the device, accurate and efficient intelligent document search can be realized, and personalized search requirements of users are met.

The application discloses an information search method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: in response to a search triggering operation of a user, acquiring a page route of a current page; according to the page route, determining a plurality of labels corresponding to the page route and a to-be-searched keyword input by the user; and searching the to-be-searched keyword under the plurality of labels to obtain a search result corresponding to each label. The embodiment can realize directional vertical search, provide personalized search service, meet different search requirements, and improve search efficiency and accuracy.

The invention discloses a group-enhanced personalized search method and system, and relates to the field of document search. The method specifically comprises the following steps: obtaining a short-term static interest vector of a user according to a plurality of historical queries of a current query and a click document list; obtaining a long-term static interest vector of the user according to the historical session; obtaining a short-term dynamic interest vector and a long-term dynamic interest vector of the user based on the query; based on the current query of the target user and the user representation vector, finding a plurality of similar related user groups to be matched with the candidate document list; performing correlation scoring on the number of clicks of the candidate document list in the current query, the number of clicks of the candidate document list in the search history, the click entropy of the candidate document list in the current query and the user matching degree of the plurality of related user groups and the candidate document list; the list of candidate documents is ranked based on the relevance score. According to the method, each query can be distinguished by combining the user query and the document feature representation, and the performance of personalized dynamic search is improved by fusing group user information.

The invention discloses an information searching method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: in response to a search trigger operation of a user, obtaining a page route of a current page; according to the page route, determining a plurality of labels corresponding to the page route; determining a to-be-searched keyword input by the user; in response to the fact that the user determines one label in the multiple labels, the to-be-searched keyword is searched under one label, and a search result list corresponding to one label is returned. According to the implementation mode, directional vertical search can be achieved, personalized search services can be provided, different search requirements are met, and the search efficiency is improved. And the search efficiency and accuracy are improved.

The application relates to the technical field of knowledge matching retrieval, and particularly discloses a vectorized knowledge representation and large model matching knowledge base system, which comprises a structured analysis module, a knowledge item vectorization module, a vector multidimensional matching module and a comprehensive weight matching module.The structured analysis module is used for obtaining all structured comprehensive character class knowledge items in a knowledge base; the knowledge item vectorization module is used for converting each structured comprehensive character class knowledge item into a high-dimensional vector based on a vectorization model; the vector multidimensional matching module is used for generating a user query vector, calculating the multidimensional matching scores of the user query vector and the high-dimensional vectors of each comprehensive character class knowledge item in the knowledge base; the comprehensive weight matching module is used for giving weights to the multidimensional matching scores based on the semantics and position constraint degrees of all synonymous entity combinations in a user query domain knowledge graph, obtaining a matching result based on the multidimensional matching scores and the multidimensional given weights, and generating a personalized retrieval report based on the matching result; and the application can improve the knowledge matching and retrieval precision and provide high-quality retrieval results.

This invention provides a personalized search and recommendation method based on knowledge forests and user preferences, relating to the fields of recommendation knowledge representation and search technology. The method includes: collecting historical information from several users and constructing a knowledge pattern tree for each user according to their needs; using an entity relation extraction algorithm to extract entity relations from the user's historical information, and constructing a knowledge tree using the extracted entities and the knowledge pattern tree, and then constructing a knowledge forest using all users' knowledge trees; using graph embedding technology to assign weights to each knowledge tree in the knowledge forest, obtaining the final knowledge forest and saving it to a graph database; constructing a target user's knowledge tree according to the target user's needs, calculating the similarity between the target user's knowledge tree and each knowledge tree in the knowledge forest by calling the knowledge forest in the graph database, and generating a personalized recommendation list for the target user, thus improving the accuracy and efficiency of inference search.

This invention provides a differentiated learning content search method based on knowledge state assessment, belonging to the field of intelligent education technology. The method includes: analyzing learning resources based on a pre-set knowledge graph, labeling related knowledge point nodes and difficulty levels, and constructing a three-dimensional virtual coordinate space to complete resource mapping; obtaining users' historical exam scores and browsing records to generate a knowledge state profile and personalized search vectors; parsing search keywords to generate constraints and initial search points, performing vector-oriented retrieval, and capturing suitable resources through Euclidean distance thresholds and constraints; generating personalized tags based on the captured vectors; calculating recommendation scores by integrating difficulty matching degree and interaction intensity, and outputting tagged results after sorting. This invention achieves end-to-end personalization from resource retrieval and sorting to presentation, providing differentiated learning content based on learners' actual ability levels, effectively solving the problems of homogeneous results and lack of adaptability in traditional search systems.

The application discloses an interactive clothing image retrieval method and system based on attribute feature disentanglement, an image acquisition module acquires clothing image data to be retrieved, and transmits the acquired clothing image data to an image preprocessing module for processing, key feature information is extracted from the preprocessed image, various attribute information and feature information of the image are predicted based on the extracted feature information, the predicted attribute information is edited, modified and deleted, the extracted feature information is stored in a database, similar images are found from the database according to a keyword or a sample picture input by a user, and the similar images are returned to the user, so that the user can quickly and accurately find the clothing image that the user wants. The application solves the attribute change retrieval problem and the dimension disaster problem, and can solve the personalized retrieval of clothing images with different attributes and the dimension disaster problem in the multi-attribute joint retrieval through disentanglement expression and memory matrix blocks.