312 results about "Preference data" patented technology

The invention relates to the technical field of data recommendation, in particular to an AI-based multi-platform media content optimization recommendation method and system. The method comprises the following steps: acquiring user equipment parameters and user platform behavior data; user preferences are confirmed based on the user platform behavior data, and user platform preference data are obtained; analyzing the hardware performance of the equipment used by the user according to the parameters of the user equipment, grading, and generating an equipment suitability matrix according to a grading result; performing user active period extraction on the user platform preference data to obtain user active period feature data; performing content loading time consumption analysis on the equipment suitability matrix to generate equipment time consumption data; and extracting format adaptation parameters of the to-be-recommended content based on a preset cross-platform content feature library. According to the method, the compatibility and the response speed of cross-platform media content recommendation are improved by comprehensively considering the user preference, the equipment performance, the time period activeness, the content adaptability and the network fluctuation.

The invention discloses an advertisement putting strategy optimization method and system based on preference data collaboration, and relates to the technical field of advertisement putting, and the method comprises the steps: collecting the internal user data of a local advertisement putting platform, obtaining the external user data of a third-party platform through a safety data interface, carrying out the integration and preprocessing of the data, and obtaining an advertisement putting strategy; extracting explicit preference data and implicit preference data; a user-advertisement scoring matrix is constructed based on the preference data, and user preference vectors are generated after decomposition; acquiring copywriting text, image and category information of an advertisement to be put, constructing an advertisement feature expression model and generating an advertisement feature vector; calculating a matching degree score based on the user preference vector and the advertisement characteristic vector, and dynamically optimizing an advertisement putting strategy according to the score; according to the method, through comprehensive utilization of explicit and implicit preferences of the user, more accurate user interest modeling and advertisement matching are realized, and the advertisement putting effect and putting efficiency are effectively improved.

The invention relates to the technical field of video generation, in particular to an intelligent commodity video generation method based on multi-modal analysis and a dynamic narrative architecture, which comprises the following steps: inputting original commodity data and a user behavior log, and outputting a dynamic selling point weight vector through a selling point value evaluation engine; s2, inputting the dynamic selling point weight vector output in S1 into a narrative flow generator, and executing the following steps: intercepting first K core selling points according to the weight vector to form a narrative trunk chain; based on the historical preference data of the user, inserting an emotion enhancement node to generate a branch enhancement narrative flow; generating a multi-version narrative flow instruction set in combination with the real-time network bandwidth data; retrieving a preset video clip library according to the instruction set to generate an initial video sequence; detecting semantic fault regions between adjacent segments to generate compensation animation parameters; and injecting compensation animation parameters and outputting a continuous narrative video stream. According to the invention, the jamming feeling and the frame skipping phenomenon caused by the change of a narrative structure or different sources of video clips are reduced, and the overall watching experience of a user is improved.

The invention discloses an intelligent parking method and device based on multi-source data fusion and a medium, and relates to the technical field of big data technologies. The method comprises the following steps: collecting multi-source parking data, and analyzing the multi-source parking data to generate a parking lot label library; generating a user tag based on historical search records and residence time preferences of the user, and constructing a user information database; when it is detected that a user triggers a parking request, performing multi-dimensional matching calculation on a user tag and a parking lot tag library to obtain a recommended parking space sequence; and optimizing the recommended parking space sequence based on the real-time traffic state and the user credit score to generate a final navigation path. According to the method, a dynamic parking recommendation mechanism is constructed by collecting and analyzing the multi-source parking data and the user preference data.

The invention relates to a costume design system and method based on multi-modal AIGC and a storage medium thereof. Comprising a multi-source input processing module used for receiving and processing multiple modal inputs including costume design text description, a costume style drawing, a fabric sample image and user preference data; the clothing feature extraction module is used for extracting clothing structure features and visual style features from the multi-source input; the semantic understanding module is used for carrying out semantic understanding and style analysis on the clothing features; the multi-modal fusion module is used for mapping the clothing feature representations of different modals to a unified semantic space and generating a fusion feature vector; and the style migration module is used for realizing design generation of a specific style based on a small number of garment samples through a LoRA low-rank adaptation technology, and is used for generating a garment design drawing through a multi-stage conditional diffusion model based on the fusion feature vector.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent content evaluation and optimization method and system based on multi-standard preference learning, and the method comprises the steps: constructing target user preference data, and generating an evaluation track containing evaluation rules and judgment results; based on the evaluation trajectory, screening samples and distributing credits through sorting and consistency rules to obtain preference pair training data; jointly training a generative reward model by adopting response supervision fine tuning and a direct preference optimization strategy; recombining the original evaluation trajectory into alternate accepting and rejecting samples, forming long thinking chain training data, and further training to obtain a final model; and evaluating and optimizing the alignment degree of the generated text and the user preference through the final model. According to the method, through multi-stage optimization and process supervision, the alignment degree and the overall performance of the language model and human preference are improved, the problems of composite errors, data sparseness and the like of a traditional reward model are solved, and the method is excellent in performance in out-of-distribution evaluation.

The invention provides a reward model training method and device, a strategy model training method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining a preference data pair which comprises a preferred response and a non-preferred response generated for the same prompt word, and each of the preferred response and the non-preferred response is composed of a plurality of text unit sequences; inputting each text unit sequence into a to-be-trained reward model to obtain a predicted reward value; and calculating the total training loss according to the predicted reward value, and updating the model parameters of the to-be-trained reward model. According to the method, the response text is subjected to serialized splitting, and the preference data composed of the preferred response and the non-preferred response is introduced for comparative learning, so that the target of model training is no longer to evaluate the absolute quality of a single response, but to identify a key text unit which causes one response to be superior to the other response; the fine-grained evaluation of the response content is realized, and the evaluation accuracy of the reward model and the identification capability of complex user preferences are effectively improved.

The invention discloses a healthy drink recommendation system based on artificial intelligence, and relates to the technical field of health preservation and health, and the healthy drink recommendation system based on artificial intelligence comprises a central control module, a data acquisition module, a data processing module, a recommendation generation module and a user feedback module. According to the system, the body data, the beverage preference data and the environment data of the user are obtained through the data acquisition module, the extracted data are processed through the data processing module, the beverage features are matched with the user requirements through the recommendation generation module, the recommendation list is generated, the recommendation result is displayed through an interaction interface in the user feedback module, and the user experience is improved. According to the method, the user can conveniently select proper beverage supplies according to the body health condition of the user, the problems that in the prior art, functional component analysis in a beverage recommendation scene is insufficient, and drinking time and drinking frequency are not fully considered are solved, and the accuracy and adaptability of a recommendation result are enhanced.

The invention discloses an AI system for generating customized contents based on user preferences, which belongs to the technical field of artificial intelligence, and comprises a user preference modeling module for constructing and continuously updating a user feature vector library and realizing personalized demand modeling and classification; the interactive input and early warning module is used for receiving user input, implementing risk control and providing a visual interactive correction tool; an intelligent parameter conversion module; a video preprocessing and enhancing module; a cross-modal semantic generation module; the dynamic training and optimizing module is used for controlling a model training process and balancing repair quality and style migration; the multi-version generation and evaluation module is used for outputting a differentiated repair result and quantitatively evaluating the performance; the feedback learning and iteration module is used for collecting user preference data and driving the system to continuously optimize; and a distributed task scheduling module. According to the method, the real-time performance and accuracy of the user portrait are ensured through the layered modeling mode, accurate matching of personalized content generation is supported, and user experience and content recommendation are effectively improved.

The invention discloses a music intelligent recommendation and music data sharing method and system, and particularly relates to the technical field of music recommendation. Music playing data, search data and user interaction data of a user on a plurality of music platforms are integrated, a global music preference model is constructed, a personalized recommendation list is generated in real time, the recommendation accuracy is dynamically evaluated and optimized, and through a safe and efficient data sharing and migration mechanism, the user experience is improved. Sharing the music preference data of the user among different platforms; meanwhile, the synchronization efficiency and the network stability in the data transmission process are analyzed through fuzzy logic, the safety and the stability of data sharing are ensured, the problems that scattered data are difficult to integrate, recommendation is not accurate and data sharing is not safe are effectively solved, and the accuracy and the user experience of the music recommendation system are remarkably improved.

The invention discloses a biochemical experiment automation script training generation method based on RAG and DPO. The method comprises the steps that biochemical experiment technical documents are collected to serve as an external knowledge base; the BM25 and the Fiss are fused to construct a hybrid retriever; generating experimental process description by using a large language model, determining equipment, protocols and materials, and retrieving related documents from a knowledge base on the basis of the experimental process description, the protocols and the materials; generating an experiment script based on a retrieval result, and performing platform simulation verification, and performing iterative optimization on the script which fails in verification; marking successful and failed scripts as preference data pairs, and constructing a training set; carrying out LoRA fine tuning on a local large language model by adopting direct preference optimization; and according to a target and appliance prompt input by a user, generating a verified experiment script by using the large language model after direct preference optimization training. According to the method, the script generation automation level and the passing rate are remarkably improved, the manual intervention cost is reduced, and an efficient solution is provided for automation of biochemical experiments.

The invention belongs to the technical field of intelligent control, and discloses an energy optimization management system and method for hotel guest rooms, and the system comprises a central data pool unit which constructs a time sequence database cluster, and carries out the uploading and interaction of data between all units through a data bus; the existence sensing unit is used for integrating multiple sensors by adopting a sensing layer architecture to form a sensing network, capturing existence sensing data and generating a room existence state map based on the existence sensing data; the existence sensing data comprises guest room reservation information data, guest room environment data and guest information data; the partition environment control unit is used for obtaining customer preference data by analyzing a room existence state map; based on the guest preference data and the guest room environment data, selectively activating environment control of different guest room areas by adopting an intelligent optimization algorithm; and the hotel energy utilization rate and customer satisfaction are improved.

The present disclosure provides a system for generating artist-governed merchandise derivative works, comprising at least one processor and memory storing instructions that, when executed, cause the system to receive a request associated with source content and a transformation theme for creating a merchandise derivative work, evaluate the request against pre-generation preference data associated with a content authority using a knowledge agent maintaining brand guidelines, generate the merchandise derivative work from the source content according to the transformation theme by applying a generative artificial-intelligence model through an action agent, perform post-generation validation against the brand guidelines, generate a digital identifier based on the merchandise derivative work, and enable distribution through a physical merchandise path for tangible products or a virtual merchandise path for digital assets.

The invention provides a remote internet-of-things control system and control method for home equipment, and the method comprises the steps: obtaining the operation data, environment data and user operation data of each piece of home equipment in a home system through a data obtaining module, and carrying out the edge calculation of the data; and online learning is carried out according to the processed data by adopting a deep reinforcement learning method through an instruction generation module, and an equipment control instruction set is constructed. The instruction verification module verifies the feasibility of each instruction in the equipment control instruction set through the preset virtual twin model based on the historical fault data and the user preference data corresponding to the home system, determines the initial control instruction set, and avoids the problem of equipment misoperation. The network state is monitored in real time through the network self-adaptive transmission module, a transmission path is dynamically adjusted according to factors such as network delay and packet loss rate, and it is ensured that a control instruction can be timely and accurately transmitted to home equipment. The instruction transmission delay or loss caused by network problems is avoided, and the user experience is improved.

The invention relates to the technical field of finance, and provides a time series data prediction method and system based on an auto-reflection mechanism large language model, and the method comprises the steps: firstly collecting structured transaction data and public unstructured text information in a preset historical time window, inputting a first large language model, and generating structured market background information in combination with a preset cue word; inputting the data and preset risk preference data into a second large language model to generate first causal reasoning training data; and inputting a third language model for reflection correction, checking whether the argument can be verified in the background information, and if so, reserving and strengthening to obtain second causal reasoning training data. And constructing a prediction large language model, optimizing parameters by means of a group relative strategy optimization algorithm, and then outputting the parameters. The system and the method depend on an auto-reflection mechanism, the model considers risk preferences in financial time sequence prediction to generate various risk strategies, manual annotation dependence is greatly reduced, the cost is reduced, and the risk preference module is independent and configurable and can be flexibly expanded.

The invention provides an optimization method, an interaction method and a system of a large language model. The optimization method comprises the steps that a preference data set is obtained, the preference data set comprises a plurality of preference pairs, one preference pair comprises question samples, preference answer samples and non-preference answer samples, the preference answer samples and the non-preference answer samples correspond to the question samples, and dynamic target reward difference values corresponding to all the preference pairs are determined, the dynamic target reward difference values corresponding to the at least two preference pairs are different, the large language model is optimized according to the dynamic target reward difference values, an optimized large language model is obtained, and the optimized large language model is used for determining a target answer meeting the preference of the target user according to the obtained target question of the target user. The optimized large language model not only can well distinguish preference answers from non-preference answers, but also can be finely adjusted according to different preference intensities, so that the personalized requirements of target users can be better met in practical application.

Devices, systems and process for automated event ticketing are disclosed. A system includes: a user device having a data store and a processor coupled to the data store; a set top box (“STB”), coupled to the user device, having an STB data store and an STB processor; and at least one event server, coupled to the STB providing event data and event ticketing data for a plurality of events. Based on user preference data received from the user device, the STB processor searches the plurality of events for a given event which matches, at least in part, the user preference data, and when instructed by the STB, the event ticketing engine facilitates ticketing of the given user to the given event. When executed, first computer instructions instantiate a content application that performs content monitoring and user preferencing, and based on the foregoing generates user preference data.

The invention discloses a direct preference optimization-based large model post-alignment training method and system. The method comprises the following steps of: (1) constructing a static reference model and a strategy model; (2) optimizing preference data by using an implicit reference model, and (3) adjusting a strategy model by optimizing an objective function so that the strategy model better fits the user preference. According to the method, the limitation of the existing DPO and SimPO methods is solved by introducing a new reference model expression, for example, the method depends on a suboptimal reference model or uses a fixed reward margin. By adjusting the balance between the strategy model and the reference model, personalized reference model setting for each pair of responses is realized, and meanwhile, a theoretical guarantee is provided and the KL divergence is effectively controlled. And the alignment performance and the winning rate of the model are remarkably improved, and the method is a robust LLM fine tuning method.

The invention relates to the technical field of Internet information processing, in particular to a sound effect recommendation method, system and device based on user preference and scene awareness, and the method comprises the steps: carrying out the deep analysis of collected user preference data, and constructing a user preference model; when an audio recommendation request of a user side is received, scene perception information matched with a current vehicle position is obtained, and a current scene is determined; matching the current scene with the user preference model to obtain a candidate sound effect set matched with the current scene and the user preference; checking each candidate sound effect data in the candidate sound effect set, screening the candidate sound effect data according to the sound effect data, and generating a recommendation result; and feeding back the recommended sound effect contained in the sound effect recommendation result. According to the scheme, the personalized requirements of the user can be fully considered, and the preferable sound effect recommendation information is provided for the user, so that the satisfaction degree of the user on sound effect recommendation is improved.

The invention discloses a model training method, a voice recommendation method and electronic equipment. The model training method comprises the following steps: clustering user data of a target user obtained from a plurality of data sources to obtain user tag system data; according to the user tag system data, obtaining corresponding user preference data of the target user in the plurality of data sources; and migrating the user preference data of the target user corresponding to the plurality of data sources to the vehicle-mounted voice scene through migration learning to obtain sample data, training the initial prediction model through the sample data to obtain a user preference prediction model for predicting the user preference data in different voice conversation scenes in the vehicle-mounted voice scene, and predicting the user preference data in the vehicle-mounted voice scene according to the user preference prediction model. The user preference data of the user in different vehicle-mounted voice conversation scenes are predicted through the user preference prediction model, voice recommendation is carried out based on the user preference data, and personalized vehicle-mounted voice recommendation is realized.

The invention relates to the technical field of internet advertisement marketing, in particular to an internet advertisement marketing method and system based on artificial intelligence, and the method comprises the steps: integrating the browsing history, demographic information and behavior preference data of a user, generating a dynamic user portrait, and predicting a user demand; generating personalized advertisement creative content based on the user portrait and the predicted user demand; performing multi-dimensional automatic violation review on the advertisement creative content, and performing originality verification on the advertisement creative content passing the violation review; based on the dynamic user portraits and the advertisement putting targets, advertisement creative contents passing violation review and originality verification are accurately pushed; according to the method, the advertisement can be automatically examined before the advertisement is put, so that the examination efficiency is improved, the compliance risk caused by omission due to manual detection and the brand reputation loss caused by the compliance risk are reduced, and the advertisement marketing effect can be improved.

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.

A method for providing personalized non-profit venue visit recommendations to a visitor at a non-profit venue, comprising: providing a management interface for management of a set of multimedia assets; receiving a mapping of multimedia assets to a display in at least one site plan; receiving metadata for the set of multimedia assets; providing an interface for receiving data indicating personal interests of a visitor, including visitor-provided preference data and passively-collected visitor interaction data; receiving personal interest data for a plurality of non-profit venue visitors; receiving preference data relating to a prospective visitor; applying a machine learning system to analyze the metadata, the data indicating personal interests of the visitor, the personal interest data for the plurality of non-profit venue visitors, and the preference data relating to the prospective visitor; and generating a selection or sequence of non-profit venue location recommendations for the prospective visitor.

The invention discloses a cross-language migration-based low-resource programming language corpus enhancement method, which comprises the following steps of: receiving a source language code, and driving a large language model to generate an initial target language code through a two-way retrieval mechanism fusing example guidance and knowledge constraint; secondly, constructing an automatic iterative repair closed loop by utilizing the feedback of a compiler, and carrying out self-correction on codes which fail to be compiled; thirdly, high-quality codes are screened out through automatic quality gating and fed back to a corpus and a knowledge base, and self-enhancement circulation of data and knowledge is formed; finally, the method further comprises an offline model evolution step based on grammar and semantic alignment driven by a compiler, a big language model is trained by collecting preference data generated by an online process, and the ability of the big language model to understand a target language is improved fundamentally. According to the method, the core problems of low quality, poor efficiency and lack of self-evolution ability of a low-resource programming language in code migration are solved.

The invention provides a book acquisition and buying regulation and control method and device based on borrowing data, and electronic equipment, and the method comprises the steps: generating an initial book acquisition and buying list of a target library based on the regulations of a middle and primary school library and the collection data of the target library; storing the borrowing data, corresponding to different data sources, of the target library into a target database in a classified manner, so as to associatively store the borrowing data of the same book through the target database; obtaining attribute information and preference data of the reader, and determining a target book based on the borrowing data and the classification information in the target database as well as the attribute information and the preference data; and determining the number of purchased copies of the target book based on the target borrowing data and the target classification information of the target book in the target database and the library collection data, and updating the initial book purchasing list based on the number of purchased copies of the target book. According to the invention, the efficiency and accuracy of book purchasing can be improved.

The invention relates to an intelligent decision-making method and system for server customization demand matching. The method comprises the steps of obtaining a server customization demand input by a user; constructing a multi-dimensional demand portrait based on the server customization demand, wherein the demand portrait comprises a quantitative index and a configuration constraint condition; according to the demand portrait, executing configuration compatibility check, and identifying whether a compatibility problem exists or not; when the compatibility check is passed, a candidate configuration scheme is generated by adopting a multi-target genetic algorithm based on the demand portrait passing the compatibility check; and sorting and screening the candidate configuration schemes based on user preference data in the demand portraits passing the compatibility check to obtain a recommended scheme list, and transmitting the recommended scheme list to a display interface for a user to select and confirm, the recommended scheme comprising a component list, a price, a performance index and a recommendation reason. The method has the effect of improving the accuracy of server customization recommendation.

The invention provides a promotion effect real-time analysis method based on multi-dimensional data, and the method comprises the steps: obtaining a shared content draft currently generated by a user through a camera application, recognizing the integrating degree of a core theme in the content and a platform content theme tendency based on a personalized text description template, generating a text description first draft conforming to a target platform style, and analyzing the promotion effect of the user according to the text description first draft. Outputting the optimized first version of the shared content; and if the user manually adjusts the first version of the shared content, recording the editing behavior details of the user, analyzing the relevance between the adjustment direction and the platform text format specification and the platform manifold use frequency, updating the platform tone style preference data in the user behavior feature library, and obtaining a user sharing habit data set.

The invention relates to the technical field of data mining, and provides a multi-modal data-based health care travel destination recommendation method and system, and the method comprises the steps: obtaining user preference data through user historical data, and obtaining time sequence health data through health equipment data; obtaining a user health portrait, and determining a demand weight through the user health portrait; obtaining destination resource data, and evaluating the destination resource data to obtain a first candidate destination set; enabling the first candidate destination set to interact with the user, obtaining preference candidate destinations through interaction parameters and the updated health equipment data, and performing recommendation logic adjustment by the user to obtain second candidate destinations; and performing route planning according to the second candidate destination to obtain an initial travel scheme, filtering and adjusting the initial travel scheme to obtain a target travel scheme, and arriving at the second candidate destination according to the target travel scheme. According to the invention, a health care tourist destination conforming to health conditions and preferences can be provided for the user.

The invention provides an electronic bidding document generation method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a bidding demand text issued by a bidding party, a historical bidding document material set accumulated by a bidding party, and bidding demand change records of similar projects in an industry; and constructing a bid inviting scene semantic map containing demand nodes, multi-modal material nodes, trend association nodes and inter-node dynamic semantic association strength information. Generating an electronic bidding document dynamic framework according to the bidding scene semantic map, and calling a cross-modal artificial intelligence content generation model to generate a multi-modal module content first draft of each function module unit; and then semantic consistency adjustment and connection optimization processing are carried out on the first draft of the multi-modal module content to obtain the integrated multi-modal electronic bidding document content. And finally, in combination with the presentation specification information and the industry bidding preference data, carrying out structural typesetting optimization and content adaptation adjustment processing on the integrated content to obtain a final multi-modal electronic bidding document, so that the quality and pertinence of the electronic bidding document are remarkably improved.

The invention provides a financial service recommendation method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology. The method comprises the steps of obtaining a user portrait label, behavior preference data of a user and a product portrait label; processing the behavior preference data of the user to obtain keyword label data representing the type characteristics of the financial service and / or the financial product, scoring the keyword label data, determining label integral data, and determining a grade score of the keyword label data based on the label integral data; and performing matching on the basis of the user portrait label and the product portrait label every preset duration, determining a target financial service and / or a target financial product, performing evaluation on the target financial service and / or the target financial product on the basis of the grade score, and determining a to-be-recommended financial service and / or a to-be-recommended financial product. According to the method, the recommendation accuracy and efficiency can be improved, and the customer satisfaction can be improved.