184 results about "Preference analysis" patented technology

The invention discloses an e-commerce platform commodity recommendation method and system based on user preference analysis, and relates to the technical field of e-commerce platforms, and the method comprises the steps: constructing a user multi-modal interest feature vector; the multi-modal interest feature vector fuses the text search history, the image browsing preference, the audio consumption habit and the video interaction behavior of the user; utilizing a cross-modal attention mechanism to match the multi-modal interest feature vector of the user with multi-modal attribute information of commodities to generate a candidate commodity set; and applying a dynamic context-aware reinforcement learning model to reorder the candidate commodity set according to real-time interaction behaviors of the user and environmental factors, and outputting a final recommendation result. According to the method, multi-modal interest feature extraction, dynamic context perception and reinforcement learning technologies are combined, so that comprehensive capture and accurate modeling of user interests are realized.

The invention relates to the technical field of data processing, and discloses a cross-border e-commerce commodity recommendation system and method based on multi-source data fusion. The system comprises an acquisition module for performing data acquisition to obtain a cross-border unified data warehouse and federal learning cooperation data; the classification module performs text classification processing to obtain a user preference analysis result and an interpretable attention mark; a quantization module carries out quantization processing to obtain a cross-border selection feature matrix; the fusion module carries out weighted fusion processing to obtain a basic comprehensive score and a weight convergence detection result; the evaluation module performs risk evaluation processing to obtain risk probability distribution and cross-border compliance evaluation results; and the sorting module carries out real-time processing through lightweight preprocessing and a flow-type calculation pipeline to obtain a comprehensive score sorting list of cross-border selected products. The problem that a traditional cross-border e-commerce product selection method cannot effectively integrate multi-source heterogeneous data and cannot analyze user feedback text semantic information is solved.

The invention relates to an intelligent short message template generation method and system based on a large language AI model, and the method comprises the steps: extracting cross-cultural communication frequency, sentence pattern preference analysis results and screen size adaptation data from social media interaction data and equipment use logs of target audiences, and generating a cultural background distribution matrix; identifying regional culture features in the culture background distribution matrix to obtain a taboo matching result; generating an expression habit preference matrix according to the updated culture background distribution matrix; generating a preliminary short message template according to the expression habit preference matrix and the symbol semantic mapping table, and outputting an adjusted template set conforming to message length control; analyzing the acceptability of the adjusted template set on the language conciseness requirement and the interactive button layout, and generating a new template set; evaluating a text alignment mode of the new template set; and screening candidate short message templates according to the information definition score. According to the invention, the accuracy and acceptability of cross-culture short message communication can be effectively improved, and the participation degree of audiences is enhanced.

The invention discloses a jewelry style prediction method and system for user preference analysis, and relates to the technical field of data analysis, and the method comprises the steps: collecting user jewelry interaction data, mining explicit features and implicit relationships, and constructing a preference feature matrix; decomposing the matrix and introducing a regularization item to train a prediction model; carrying out transfer learning through a dynamic self-attention layer in combination with the interaction scene to generate a style prediction model; according to the user recommendation task, adjusting the feature weight and generating a jewelry recommendation portrait; and through a platform retrieval engine matching resource, generating a recommendation result and visually displaying the recommendation result. The technical problem of insufficient user preference analysis and recommendation accuracy in the prior art is solved, and the technical effect of improving the user preference analysis accuracy and recommendation accuracy is achieved.

The invention relates to the technical field of sensor identification, in particular to a sound parameter determination method and system. The method comprises the following steps: obtaining sound multi-modal sensing data, and carrying out multi-dimensional feature vector combination to obtain a high-dimensional feature data set; performing feature mapping on the high-dimensional feature data set to obtain a sound response feature map; performing environment dynamic parameter adjustment strategy analysis based on the sound response feature map to obtain an environment dynamic parameter adjustment strategy; obtaining user sound control data, and performing user auditory preference analysis to obtain user auditory preference data; and according to the user auditory preference data, performing user personalized sound adjustment feedback optimization on the environment dynamic parameter adjustment strategy to obtain an optimized feedback sound dynamic adjustment strategy, and transmitting the optimized feedback sound dynamic adjustment strategy to the sound control platform to execute a parameter adjustment task. According to the invention, the problems of subjective errors and untimely response to environmental changes in traditional manual adjustment can be effectively avoided.

The invention relates to the field of digital content generation, and discloses an AI-based digital cultural creative content dynamic generation system, which comprises a user behavior data collection module used for acquiring interactive behavior data of a user on digital cultural creative content and generating a user feedback signal; the state modeling and user preference analysis module is used for constructing a user state according to the user feedback signal and analyzing user preference information; the style control vector generation module is used for generating a style control vector according to the user state, and the style control vector is used for regulating and controlling style features of the content to be generated; and the content generation module is used for receiving the style control vector and the user state information and generating the digital text creation content. According to the method and the system, through combination of the user behavior data and the emotion feedback signal, the system can dynamically adjust the style control vector, accurate personalized customization of the content style is realized, and the requirements of different users are better met.

The invention discloses a recommendation method and device based on user interest points, and relates to the technical field of interest point recommendation, and the method comprises the steps: S1, obtaining behavior data, time information and attributes of interactive items of a user on a platform, and carrying out the preprocessing of the obtained data; and S2, performing short-term preference analysis and long-term preference analysis based on the processed user data, modeling interest points of the user, generating a user portrait and the like. According to the method, short-term preference analysis and long-term preference analysis are respectively carried out by utilizing the user data, and a unified user portrait is formed by combining short-term and long-term preference characteristics, so that more accurate personalized recommendation is provided for the user, the user satisfaction and the scene matching degree are improved, and the user experience is improved through a long-term and short-term preference fusion strategy. Therefore, the recommendation system can utilize the existing resources more efficiently and obtain more comprehensive and abundant preference representation of the user, so that more efficient and accurate interest point recommendation is realized.

The invention discloses an intelligent verification system and method for full-process review based on multi-modal analysis, and belongs to the technical field of intelligent review. Comprising a processing analysis module, an experiment extraction module, a risk early warning module, a reproduction verification module, a chart comparison module, a dispute tracing module, a feedback analysis module, a load monitoring module, a report generation module and a preference analysis module. According to the invention, comprehensive and multi-angle intelligent review can be realized, more detailed risk early warning and positioning information can be generated, the experiment reproducibility can be quickly verified at low cost, the manual verification burden is greatly reduced, the innovation and risk of the thesis viewpoint are scientifically evaluated, the fatigue of a manuscript reviewer is relieved, the manuscript review efficiency and quality are improved, and the method is suitable for popularization and application. And dynamically adjusting the review content recommendation sequence.

The invention discloses a personalized costume design recommendation system and method based on AI and big data, and relates to the technical field of personalized recommendation data processing, the system comprises an information acquisition module, a feature analysis module, a preference analysis module and a design recommendation module; the information acquisition module is used for acquiring user multi-modal information; the feature analysis module is used for extracting and fusing user preference features from the user multi-modal information and calculating a user multi-modal interest expression vector; the preference analysis module is used for carrying out joint modeling by combining a Transform network and a GRU structure based on the user multi-modal interest expression vector, and generating a user multi-dimensional dynamic preference vector through fusion of a gating mechanism; and the design recommendation module is used for outputting a personalized costume design recommendation result through a reinforcement learning model in combination with the multi-dimensional dynamic preference vector of the user.

The invention discloses a metro operation decision optimization method based on a knowledge graph and cross-modal association, and relates to the related technical field of metro operation, and the method comprises the steps: deploying an edge computing node at edge equipment of a metro, and configuring a universal weak classifier; computing preference analysis is executed, and a preference database is generated; reinforcement learning is carried out on the universal weak classifier; after communication data is received, fault decision recognition is carried out, and an edge fault decision recognition result is established; and obtaining subway operation environment data and user feedback data, and after synchronizing the data to the subway knowledge graph, generating a subway operation and maintenance decision optimization result. The technical problems that in the prior art, multi-modal big data cannot be efficiently processed, potential correlation of different-modal data cannot be fully mined, and consequently subway fault decision recognition accuracy is insufficient and operation efficiency is poor are solved, dynamically-updated and multi-modal fused subway operation knowledge graph construction is achieved, and the metro operation knowledge graph construction efficiency is improved. The technical effect of improving subway operation decision accuracy and operation efficiency is achieved.

The invention discloses a personalized travel route planning method and system based on a large language model, and a terminal. The method comprises the steps: collecting multi-source data information, carrying out the extraction and standardization of the multi-source data information, obtaining a target multi-source data set, and constructing a travel knowledge graph according to the target multi-source data set; the method comprises the following steps: constructing a big language model, performing instruction type fine tuning on the big language model according to a tourism knowledge graph and a preset fine tuning task, enhancing the big language model after instruction type fine tuning through a knowledge distillation technology to obtain a tourism route planning model, and outputting a user preference vector and a candidate scenic spot set according to the tourism route planning model. And according to a user preference vector and the candidate scenic spot set, generating a personalized travel route plan through a traditional route optimization algorithm. According to the method, entity extraction and knowledge graph dynamic modeling are carried out on the multi-source heterogeneous tourism data, deep semantic understanding and preference analysis are carried out in combination with a large language model, personalized tourism routes can be provided, and the travel efficiency of users is improved.

The invention relates to an intelligent social media post generation system and method based on a multi-modal architecture, and the method comprises the steps: firstly carrying out the subdivision of a complex demand inputted by a user through a user demand disassembly module, dividing a task into a plurality of subtasks, and constructing a preliminary thinking chain; on the basis, a multi-angle demand analysis module is used for carrying out emotion and style preference analysis on the demand, and a sub-task list conforming to the intention of the user is generated. Next, the system enters a multi-modal content interpretation module, comprehensively analyzes the uploaded data through a multi-modal large language model, and extracts a feature vector of each modal; and the content vectorization and RAG matching module performs retrieval enhancement generation matching on vectorization features, and extracts content similar to requirements from social media as a reference. And the thinking chain construction module performs logic organization and multi-angle optimization analysis on the reference content. And the social media post generation module performs style and emotion adaptive adjustment on the logically structured content to generate high-quality social media posts meeting user requirements.

The invention relates to the technical field of intelligent recommendation systems, and discloses a personalized resource recommendation method and system.The personalized resource recommendation method comprises the steps that real-time interaction logs and course resource data are collected, and preprocessing and feature extraction are conducted on the real-time interaction logs; performing structured processing on the course resource data; performing occupational skill mapping based on the layered resource data; performing feature extraction and semantic coding; performing multi-level user interest analysis; constructing a curriculum-to-skill mapping model and training, and deducing pre-repair dependence between curriculums; strategy mixed recommendation is carried out, and weighted fusion is carried out on mixed recommendation results; performing user diversity preference analysis, evaluating the diversity of the personalized recommendation candidate set, and performing optimization in combination with an evaluation result; performing multi-dimensional interpretation and interpretation quality optimization on the final recommendation result; in combination with a scene adaptive fusion mechanism, the learning requirements of the user in different time dimensions can be accurately captured, and the recommendation accuracy is improved.

The invention discloses an application preloading method and device, equipment and a storage medium, and relates to the technical field of application management, and the method comprises the steps: obtaining user behavior data of a target user and current network environment data; inputting the user behavior data into a user preference prediction model for preference analysis to obtain user preference information output by the user preference prediction model; inputting the user preference information and the current network environment data into an application recommendation engine for recommendation analysis to obtain an application recommendation list and a resource preloading strategy output by the application recommendation engine; and based on the resource preloading strategy, performing application preloading on the application recommendation list through a preset back-end cache library. According to the application, the application needing to be preloaded is selected through the user preference and the network environment, unnecessary resource loading is avoided, the resource utilization efficiency is improved while the loading time is remarkably shortened, personalized application recommendation is provided for the user, and the personalized requirement of the user is met.

The invention provides a push time determination method, a program product, electronic equipment and a storage medium. The method comprises the following steps: acquiring an original feature of a to-be-predicted user; the original features comprise user information features of the to-be-predicted user and interactive behavior features of the to-be-predicted user in a plurality of time windows; according to the user information features and the interaction behavior features, obtaining cross features of the to-be-predicted user in the plurality of time windows; inputting the cross features of the to-be-predicted user in the plurality of time windows into a pre-trained user preference analysis model to obtain preference degrees of the to-be-predicted user in the plurality of time windows; and based on the preference degrees of the to-be-predicted user in the plurality of time windows, determining the push time. The user preference analysis model learns the behavior patterns of the to-be-predicted users in different time periods, predicts the preference degrees of each to-be-predicted user in different time windows, namely predicts the probability that the to-be-predicted user clicks push information in each time window, and determines a more accurate push opportunity.

The invention belongs to the field of big data, and relates to an exclusive poster generation method, which comprises the following steps of: displaying a poster preview two-dimensional code, and triggering to obtain user information of a target user at a plurality of application ends when a scanning operation aiming at the two-dimensional code is detected; performing preference analysis on the user information to obtain preference data; based on the preference data, determining to-be-recommended content and a plurality of corresponding poster elements; obtaining poster style reference information, and generating poster dynamic elements based on the to-be-recommended content and the preference data; and based on the preference data, obtaining a target poster template from a poster template library, and generating an exclusive poster of the target user in combination with a plurality of poster elements, poster style reference information and poster dynamic elements. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and the user information can be stored in a block chain. The method can be applied to the business fields of financial insurance, medical health, old-age care and the like, and personalized exclusive posters can be customized for different users.

The embodiment of the invention belongs to the field of financial science and technology, and relates to an information display method and device, computer equipment and a storage medium, and the method comprises the following steps: obtaining real-time business data and target equipment parameters; performing feature extraction on the real-time business data to obtain business data features; performing modeling and information construction on the target equipment parameters based on a decision tree classifier to obtain equipment portrait information; generating an equipment adaptive page according to a predefined page template, the business data features and the equipment portrait information; optimizing the equipment adaptive page according to the user page interaction information to obtain an optimized adaptive page; performing user preference analysis on the user portrait information based on a collaborative filtering algorithm to obtain a user recommendation information list; and fusing the user recommendation information list and the optimized adaptive page, and sending the fused user recommendation information list and the optimized adaptive page to a user interface for information display. According to the method and the device, the corresponding page display information can be effectively generated according to the equipment and the information preference of the user to be displayed.

The invention provides a jeans appearance emotional preference analysis method, and belongs to the technical field of emotional analysis, and the method comprises the steps: collecting massive real and dynamic user online comment data to obtain an original comment data set, and carrying out the preprocessing to form a structured text corpus; performing topic modeling on the preprocessed text corpus by utilizing a potential Dirichlet distribution model, introducing a domain knowledge filtering mechanism, and establishing an optimal sentiment keyword set of the appearance of the jeans; then calculating a weight value of an emotion keyword in each theme; and finally, based on an emotion keyword weight calculation result, performing fuzzy operation on the candidate design schemes through a fuzzy comprehensive evaluation method to obtain emotion preference scores of different design schemes, and realizing quantitative evaluation and optimization of the design schemes. According to the method, the emotion keyword recognition efficiency and accuracy can be improved, the multi-emotion demand priority of the user is defined, the emotion preference score of the user for the product design scheme is output, and the optimal design scheme meeting the emotion demand of the user is provided.

The present solution relates to methods for generating images. The steps of the present method include preliminary training of a neural network and textual tagging of elements on an image by loading a dataset consisting of a plurality of images of a given element, with links being established to an ontology. The images are input to a CLIP neural network for tagging, where a parallel analysis of an image and a textual description is performed, resulting in the creation of a feature vector. Analysis of the loaded images is carried out by virtue of the segmentation of the images into a plurality of iterations by the isolation of large objects. A mask with a segmented image is produced, then the isolated objects are classified by the neural network. At the same time as the objects are identified, the image is tagged accordingly. In a final step, images are generated from an existing library of images, wherein each of the images has a set of tags that determine whether it can be used in a particular composition. The invention makes it possible to generate images using machine learning algorithms and to improve the quality of image generation.

The invention relates to the field of image generation, in particular to an AIGC-based advertising map generation method, which comprises the following steps of: periodically detecting an associated element proportion and an associated element complex coefficient of a target generated image in each target image combination so as to determine a combination state of each target image combination; determining an image analysis strategy according to the combination state; when an element combination analysis mode is adopted to extract optimal generation elements, the optimal generation elements are determined according to the reference evaluation coefficients, and the combination coefficients of the key analysis elements are adjusted; when the preferential generation elements are extracted in a preference analysis mode, the preferential generation elements of the target image combination are determined according to the reference evaluation coefficient and the reference use frequency; under the image generation condition, the target combination elements are determined according to the optimal indexes, the target element set is determined according to the reference combination coefficients, and the effectiveness of the image synthesis result and the image synthesis efficiency are improved.

The invention provides a user preference analysis method and device oriented to right and interest products, belongs to the field of intelligent recommendation algorithms, and is used for solving the problem that the right and interest product ordering rate is low in the related art. The method comprises the steps that behavior feature data of a target user is obtained, the behavior feature data carries a timestamp and a right and interest product identifier, and the timestamp and the right and interest product identifier are sent to the target user; the behavior characteristic data comprises a characteristic value of each behavior characteristic dimension; substituting the behavior characteristic data of the target user into a self-researched preference analysis model to obtain preference degree data of the target user for each right and interest product; according to the invention, the preference degree data of the target user for each right product can be intelligently and reasonably analyzed based on the behavior characteristic data of the target user, and the quantitative preference of the target user for the right product can be accurately analyzed with low cost and high efficiency, so that the ordering rate of the right product can be improved with low cost and high efficiency.

The invention discloses a service platform for completing bidding document production based on automation technology assistance, and belongs to the technical field of bidding services, and the service platform specifically comprises the steps that a user behavior collection module collects operation behavior data of a user in the bidding document production process; the decision preference analysis module processes the operation behavior data through an analysis model to generate user decision preference parameters; the strategy generation module generates a candidate bidding strategy set based on historical cases; the scene simulation module constructs a bidding scene simulation environment and records market competition data in a strategy execution process; the strategy evaluation module calculates a strategy competitiveness index according to the market competition data; the strategy execution module screens an optimal bidding strategy and converts the optimal bidding strategy into a specific bidding file making scheme; through intelligent analysis of user behaviors and dynamic simulation of a bidding environment, the accuracy and competitiveness of bidding document production are effectively improved.

The invention relates to the field of data analysis, in particular to a tourism data analysis method and system based on deep learning. The method comprises the following steps: acquiring initial tourism data in Internet of Things equipment, performing data preprocessing on the initial tourism data, and performing feature extraction on standard tourism data by using a model architecture combining a convolutional neural network and a recurrent neural network; an analysis model of tourism data is constructed by fusing multiple deep learning algorithms to analyze characteristic tourism data, analysis at least comprises tourism preference analysis of tourists, tourism peak periods, change rules of scenic spot popularity along with time and consumption mode analysis of the tourists in different regions, and the tourists are subdivided by combining a clustering algorithm. And displaying the tourism behavior characteristic group through AR virtual augmented reality, and performing real-time dynamic adjustment according to user demands. Various data sources can be integrated, particularly social media and Internet of Things equipment data are fused, and a more comprehensive tourism data perspective is provided.

The invention relates to the technical field of content recommendation, in particular to a user music recommendation method and system based on data analysis, and the method comprises the following steps: based on a music playing record of a user, extracting the playing frequency and frequency information of each piece of music, evaluating the preference degree of the user for each music type, analyzing the preference degree of the user for each music label, and recommending the user music. And obtaining a user preference analysis result. According to the method, the preference degrees of the users for different music types and labels are analyzed according to the playing frequency and frequency, the ages and genders of the users are considered, the fineness and adaptability of recommendation are optimized, user groups with similar music preferences can be identified more accurately through evaluation and classification of the similarity between the users, and the user experience is improved. A more accurate recommendation list is provided, the playing habits of the user in different time periods are analyzed, the recommendation opportunity is optimized, it is ensured that the user receives the recommendation in an acceptable time period, the participation degree of the user is improved, and the acceptance degree of the user to the recommended content is increased.

The invention discloses a cross-border e-commerce product recommendation matching system based on artificial intelligence, particularly relates to the technical field of international trade, and comprises a user behavior preference analysis module which is used for deeply knowing interests and demands of a user by analyzing behavior data and emotion feedback of the user so as to realize personalized recommendation. The user portrait is accurately constructed by analyzing the behavior data, emotion feedback and historical purchase records of the user, personalized product recommendation is realized by using deep learning and reinforcement learning technologies, and the system combines logistics, price, culture and payment preferences of the region where the user is located, so that the user experience is improved. The optimal cross-border logistics scheme, price optimization and localization payment mode are intelligently matched, it is ensured that products can be smoothly delivered and meet the requirements of local users, through real-time monitoring and user feedback collection, the system continuously optimizes the recommendation algorithm and improves the performance, the recommendation accuracy and stable operation of the system are ensured, and the user experience is improved. Therefore, the conversion rate and the user satisfaction degree are improved.

The invention relates to the technical field of language processing, in particular to an intelligent customer service recommendation method fusing multi-modal information and application equipment thereof, and the method comprises the following steps: S1, user question recognition; s2, character recognition processing; s3, carrying out image recognition processing; s4, performing voice recognition processing; s5, user questions are integrated; s6, user preference analysis; s7, analyzing user interaction records; according to the method, character recognition processing, image recognition processing and voice recognition processing are carried out according to the user question-asking modes, then user question-asking associated questions are obtained through analysis, and the recommended reply modes are obtained through analysis according to the preference degree of the user for the reply modes and the degree of interaction understanding difficulty. Through multiple identification processing modes, questions of different forms of the user can be more comprehensively captured, accurate mastering of the intention of the user is ensured, meanwhile, the preference of the user for different reply modes is considered, and habits and preferences of different users can be better adapted.

The invention relates to the field of product selection service management, and discloses a product selection service management method and system based on real-time big data calculation, and the method comprises the steps: combining the current behavior data of a user with the historical behavior data, analyzing the preference of the user, recognizing a product preferred by the user, and carrying out the calculation of the product selection service. By analyzing the similarity between the product and the product interested by the user, the product closely associated with the user is screened out, and by analyzing the whole process of the user from product contact to final purchase, the behavior path of the user is tracked in real time, so that the user experience is improved. Based on comprehensive analysis of a user preference analysis result, a product association analysis result and a user behavior path analysis result, all related products meeting user requirements are screened out, and the screened products meeting the user requirements are sorted and recommended, so that a more comprehensive and more personalized recommendation strategy can be formulated, and the user experience is improved. The accuracy and effectiveness of recommendation are improved, and diversified requirements of users are met.

The invention relates to the technical field of big data processing, in particular to a user preference analysis method and system, and the method comprises the steps: inputting a popularity value sequence of each demand of a target user into a long-short-term memory network, obtaining long-term features and short-term features of each demand, and enabling the long-term features of each demand to form a user portrait of the target user; obtaining a correlation matrix of a user group to which the target user belongs, and inputting the short-time feature of each demand and the correlation matrix into a preference analysis model to obtain a popularity attenuation coefficient of each demand; and inputting the real-time popularity value and the popularity attenuation coefficient of the target user for each demand into a preset attenuation model to predict the popularity duration of each demand, and obtaining an analysis result. Through the technical scheme of the invention, the popularity attenuation of the user for each demand can be considered, and an accurate preference analysis result can be obtained.

The embodiment of the invention provides a resource recommendation method and device, equipment and a storage medium, and is applied to the technical field of data personalized recommendation. According to the method, the local lightweight resource index pool and the local user behavior database are constructed, preference analysis and multi-dimensional scoring recommendation are directly performed on the terminal, the hysteresis of cloud computing is avoided, quick and accurate response can be realized, network dependence and server pressure are remarkably reduced, and the user experience is improved. Therefore, the problem of poor recommendation timeliness and accuracy in the existing user personalized data pushing scheme is solved.