64 results about "Preference analysis" patented technology

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 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 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 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.

The invention discloses a dynamic trapping network deployment method and system fusing attack behavior preferences, and relates to the technical field of network security. The method comprises the following steps: modeling a network topology into a directed acyclic graph, and constructing a dynamic trapping network topology structure fused with attack behavior preference based on attacker behavior preference analysis in combination with situation awareness; modeling an attack and defense confrontation process as a Markov multi-stage dynamic game process, and solving Nash equilibrium to obtain an optimal strategy of each stage; the behavior preference of an attacker is updated in real time by using Bayesian learning and a sliding window mechanism, and the adaptability of a dynamic trapping network to attack and defense situations is enhanced. According to the method, the problems of configuration stiffness and poor attack and defense situation change adaptability in dynamic trap network deployment are solved, the active defense capability aiming at attacker behaviors can be improved, and an effective solution is provided for dynamic deployment of a trap network in a dynamic network environment.

The invention provides a short video content personalized recommendation method and system based on big data, and relates to the technical field of short video recommendation. The method comprises the following steps: acquiring historical short video watching data of a user, determining a plurality of fragment watching events and constructing a plurality of watching content sequences; constructing an event behavior vector of each watching content sequence, performing watching scene division on a plurality of fragment watching events to generate a plurality of watching scene clusters, performing content adaptation analysis on each watching scene cluster, constructing a content adaptation map of each watching scene cluster, and obtaining a content adaptation map of each watching scene cluster; and performing content preference analysis on each watching scene cluster and constructing a content preference sequence, generating a plurality of video recommendation optimization units of the user based on the content adaptation map and the content preference sequence, and performing personalized short video content recommendation on the user based on the plurality of video recommendation optimization units. According to the invention, the accuracy of personalized recommendation of the short video is improved.

An object of a system according to an embodiment is to propose a way of spending leisure on the basis of a user's preference.SOLUTION: A system according to an embodiment includes an information collection unit, a preference analysis unit, and a proposal generation unit. The information collection unit collects an answer to a simple question from a user. The preference analysis section analyzes the user's answer collected by the information collection section and understands the user's preference. The proposal generation unit proposes a way of spending leisure on the basis of the preference of the user analyzed by the preference analysis unit.SELECTED DRAWING: Figure 1

An object of a system according to an embodiment is to perform matching with clothes that a user has in consideration of a preference of the user.SOLUTION: A system includes a preference analysis unit, a matching unit, and a proposal unit. The preference analyzer includes a generation AI. The matching unit performs matching with clothes that the user has on the basis of the preference of the user analyzed by the preference analysis unit. The proposal unit displays the proposal of the clothes matched by the matching unit on the digital signage.SELECTED DRAWING: Figure 1

The invention relates to the technical field of child behavior analysis, and discloses a multi-age child recreation behavior preference analysis system, which comprises a multi-dimensional acquisition module and an intelligent analysis module. According to the system, management data of urban green space and recreation behavior management data of participated children are acquired through a multi-dimensional acquisition module and are classified to form a data set, and an intelligent analysis module evaluates the popularity degree of each green space and generates an attraction index, so that data support is provided for facility configuration optimization and people flow guidance; the practical value of the green space is improved, the correlation coefficients of the age, the height and the duration of two recreation behaviors are accurately calculated, the adaptation rule of the child growth stage and the recreation behaviors is revealed, the preference index is generated, the matching level of green space configuration and child preference is accurately reflected, the comprehensive analysis precision is high, optimization measures are specifically triggered, and the method is suitable for popularization and application. Through the modes of adjusting the facility layout, dividing exclusive areas, adding facilities and the like, the greenbelt space is ensured to be adaptive to the multi-age children, and personalized management and recreation experience are good.

The application relates to the technical field of vehicle maintenance service, in particular to a personalized vehicle maintenance reminding and pushing system based on owner historical data, which obtains a plurality of vehicle use features according to owner historical data through a feature extraction module, obtains a user portrait label according to the vehicle use features through a user analysis module, obtains reference weights of the plurality of vehicle use features according to the user portrait label through a preference analysis module, obtains a maintenance cycle correction amount according to a preset multi-expert hybrid model based on the vehicle use features and the reference weights through a correction and calibration module, and then performs vehicle maintenance reminding and pushing according to the maintenance cycle correction amount through a reminding and pushing module. Compared with the prior art, the application adopts an innovative multi-expert hybrid model architecture, outputs a scientific and reliable maintenance cycle correction amount, and solves the problem that the vehicle maintenance reminding scheme in the prior art lacks personalization.

The application discloses a multi-target-oriented power distribution network safety domain operation point screening method, medium and equipment, belongs to the field of power system operation and optimal control, and comprises the following steps: firstly, constructing a static safety domain containing a power flow equation and multiple constraints based on power distribution network nodes, branch information and safety criteria; secondly, accurately obtaining a safety domain boundary through adaptive direction search and error correction; thirdly, generating a candidate operation point by Monte Carlo sampling, combining economic and reliable indexes, and obtaining a Pareto optimal solution set through fast non-dominated sorting; finally, screening an optimal operation point considering safety, economy and reliability through fuzzy multi-attribute decision and decision maker preference analysis; the application realizes three-dimensional target collaborative optimization, is high in calculation efficiency and strong in robustness, can adapt to real-time optimization requirements of high-proportion distributed power distribution networks, and can be widely applied to active power distribution network safety evaluation, energy storage configuration and economic dispatching scenes.

An object of a system according to an embodiment is to provide a family quest customized based on the preference or interest of a family member.SOLUTION: A system according to an embodiment includes a preference analysis unit, a fusion design unit, and a quest generation unit. The preference analysis unit analyzes the preferences, interests, and strengths of the family. The integration design unit designs the customized family quest in which the real world and the virtual world are integrated based on the preference, the interest, and the strength of the family analyzed by the preference analysis unit. The quest generation unit generates the family quest designed by the integration design unit.SELECTED DRAWING: Figure 1

The invention provides an AI-based intelligent audio player and a control method thereof. Belongs to the technical field of artificial intelligence and music players. The method comprises the steps of performing multi-dimensional data collection on a user of the intelligent audio player, and generating a user music preference comprehensive data set; based on the comprehensive data set, constructing an initial user music preference analysis model through a machine learning algorithm; a plurality of environment sensors are integrated on the intelligent audio player, data of a playing environment are collected in real time through the environment sensors, and environment real-time data are generated. Through multi-dimensional data collection and machine learning algorithm analysis, in combination with the song listening history, collection preference and active feedback of the user, the intelligent audio player can accurately generate a music preference comprehensive data set of the user and construct an initial personalized music recommendation model, so that the deviation between recommended songs and user preferences is reduced, and the user experience is improved. And the recommendation accuracy and personalized experience are improved.

The invention belongs to the technical field of data analysis, and particularly discloses a marketing method and system based on user behavior analysis and a program product, through a multi-dimensional behavior quantification mechanism, the preference condition of a user for each product type can be distinguished according to the corresponding behavior data of the user for different product types, the preference identification accuracy is high, and the user experience is improved. The preference analysis result can better reflect the interest of the user; the user behavior value index and the group preference similarity are fused through the comprehensive preference degree, the advantages of personalization and collaborative filtering are considered, and the product pushing conversion rate can be effectively increased; high-dimensional vector operation and clustering iteration are not needed, the calculation overhead can be greatly reduced, and the method is suitable for a real-time marketing scene; and the preference analysis result can be traced back to the specific behavior type, the interaction depth and the group portrait matching label, so that the platform can carry out marketing strategy adjustment and optimization conveniently, and the platform marketing transparency and the user credibility are improved.

The embodiment of the invention relates to a preference analysis model training method and a video recommendation method. The preference analysis model training method comprises the steps of obtaining user behavior data of a first user; generating model cue words based on the user behavior data of the first user, the plurality of video feature dimensions and the plurality of preference level features, inputting the model cue words into a large language model, and driving the large language model based on the model cue words to generate at least one group of video preference contents with preference grading tags, the video preference information serves as video preference information of the first user; constructing a training sample based on the user behavior data and the video preference information, inputting the user behavior data in the training sample into a model, outputting a predicted video preference corresponding to the user behavior data by the model, and verifying the predicted video preference by using the video preference information in the training sample, and adjusting parameters of the model according to the verification result until the model reaches a preset convergence condition, and taking the model as a preference analysis model.

The application discloses a virtual-real fusion exhibition display interaction system and a multi-modal perception method, aiming at solving the problems of single interaction mode, insufficient perception accuracy and the like of the existing system. The system comprises a virtual-real fusion display, a multi-modal perception, an interaction control, a data processing and a storage module, can load virtual content and accurately fuse with the entity scene. The multi-modal perception module synchronously collects visual, motion and voice data, identifies user intention after pre-processing, feature fusion and semantic analysis, and the interaction control module generates instructions accordingly to drive the display module to adjust the content and feedback. The method realizes efficient interaction through initialization, data collection, pre-processing, feature fusion, intention recognition, interaction feedback and preference analysis. The application improves the interaction intention recognition accuracy and virtual-real fusion accuracy, guarantees real-time response, enhances the sense of immersion and personalized service ability, and is suitable for various exhibition scenes.

The system according to the embodiment aims to provide a personalized travel plan based on the user's past behavior and preferences. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a user's past behavior or preferences. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a personalized itinerary based on the analysis results obtained by the analysis unit. The provision unit provides the itinerary generated by the generation unit.

The application provides a recommendation method and device, equipment and a storage medium. The method comprises the following steps: obtaining first behavior data of a target user; determining at least one first item to be recommended according to the first behavior data; determining a first preference of a second item associated with the first item; the first preference is determined based on usage data of at least one other user different from the target user for the second item; determining a first evaluation score of the second item; the first evaluation score is determined based on evaluation information received by the second item; and generating a first recommendation result according to at least the first preference, the first evaluation score of the second item associated with each first item and each first item. Thus, the preference analysis of the target user for the second item associated with the first item to be recommended is more comprehensive, the generated recommendation result is more accurate, and the user experience is improved.

The application discloses a kind of based on data security's user product preference analysis method and system, including the method includes the following steps: S1, obtain the browsing information and purchase information of user, and analysis obtains user behavior parameter;S2, the data cleaning of obtained user behavior parameter is carried out, and based on the type of product, the browsing and purchase data are classified, and classification behavior data are obtained;S3, according to classification behavior data, the subjective preference degree and objective preference degree of user to product are calculated;S4, according to pre-set reliability, the subjective preference degree and objective preference degree of user are merged, and the comprehensive preference degree of user to product is calculated;S5, according to comprehensive preference degree, the purchase preference of user to product is analyzed, and based on the comprehensive preference of user, corresponding product is recommended for user.The application carries out classification to user product preference value interpretation result and carries out desensitization processing to data, improves the use efficiency of the based on data security's user product preference analysis method.

Methods and apparatuses for automatic treatment planning, including recommendation systems, quality assurance, error prevention, text mining, text matching, and treatment planning optimization.

The invention relates to the field of commodity data management, in particular to a vending cabinet commodity data management method and platform based on artificial intelligence. The method comprises the following steps: collecting transaction logs of vending machines, tracing and identifying commodity access records, and constructing an access distribution map; meteorological parameters are obtained and multivariate meteorological influence sensitivity analysis is carried out, so that selling sensitivity of different meteorology is generated; identifying geographic information of a plurality of vending cabinets, performing scene-based commodity preference analysis, and generating a scene-commodity demand association relationship; according to the scene-commodity demand association relationship and the selling sensitivity, commodity fluctuation evaluation is carried out on the access distribution map, and a commodity selling fluctuation result is generated; and performing strategy iterative optimization according to a commodity selling fluctuation result, and constructing an intelligent commodity management decision model. According to the invention, the manual intervention demand is reduced, the management efficiency is improved, and the inventory turnover rate and the sales prediction accuracy are improved.

The invention provides a credible recommendation model based on a two-dimensional constraint large language model. The credible recommendation model comprises the following steps: S1, a negative description module and a similar behavior generation module: providing a multi-dimensional balance view angle for subsequent reasoning through the negative description module and the similar behavior generation module; s2, a knowledge consistency verification module: the knowledge consistency verification module enables the features of the input model to have high reliability through a lightweight fact consistency detection process; s3, a thinking chain reasoning module: a thinking chain reasoning part performs task decomposition, and a complete reasoning chain is generated from user preference analysis to final scoring; the fusion module fuses a statistical result of a traditional recommendation channel and a reasoning result of a large language model channel through self-adaptive weight adjustment, a final recommendation result is generated, the illusion and deviation problems of a large language model can be solved, and recommendation performance is enhanced by integrating a deep reasoning large language model.

The invention discloses an automobile competitive product and comparative attribute identification method based on large model enhancement, and the method comprises the steps: large model-driven information extraction, multi-view theme modeling and joint preference analysis, the method specifically comprises the following steps: 1) automatically extracting an automobile product and a feature description text from an evaluation copywriting by constructing a cue word guide large model, and carrying out standardized word segmentation; 2) on the basis of the de-duplicated products and the feature words, constructing a coupling LDA model, modeling automobile competitive product themes and product feature related themes, and depicting theme preference distribution of documents; and 3) inferring parameters by adopting collapse-type Gibbs sampling, calculating product and feature distribution under the theme, and mining preferences of competitive product combinations and comparative attributes of the competitive product combinations. According to the method, the problems of dependence on manually defined entities, sparse co-occurrence modes, difficulty in capturing fine-grained comparison dimensions and the like are solved, potential competitive product groups and key comparison attributes can be efficiently and accurately identified, and interpretable decision support is provided for enterprises to formulate differentiated product strategies.

The invention relates to a market demand prediction method and system for electricity-hydrogen coupling technology equipment, and the method comprises the steps: carrying out the energy consumption preference analysis of different user types in the potential application field of an electricity-hydrogen coupling technology, and obtaining the user portraits of all types of users; constructing a technical utility value function and a market demand function of the electricity-hydrogen coupling technical equipment based on the user portrait; wherein the market demand function is constructed based on a technical utility value function; comparing and analyzing competition elements of the electricity-hydrogen coupling technical equipment and the traditional equipment, and assigning scores to competition element parameters in the market demand function to obtain technical utility value of the electricity-hydrogen coupling technical equipment; based on the technical utility value, the market demand of the electro-hydrogen coupling technical equipment is obtained. Compared with the prior art, the method has the advantages of high market demand prediction accuracy and the like.

An object of a system according to an embodiment is to recommend optimal clothes according to a user's preference, trend, and season.SOLUTION: A system includes a preference analysis part, a trend analysis part, a season analysis part, and a recommendation part. The preference analysis unit analyzes the user's preference and past purchase history. The trend analysis unit analyzes a current fashion trend. The season analysis unit selects clothes according to the season. The recommendation unit recommends optimal clothes to the user in comprehensive consideration of information obtained by the preference analysis unit, the trend analysis unit, and the season analysis unit.SELECTED DRAWING: Figure 1

The application provides a trusted recommendation model based on a double-dimension constraint large language model, and comprises the following steps: S1, a negative description module and a similar behavior generation module: the negative description module and the similar behavior generation module provide a multi-dimensional balanced perspective for subsequent reasoning; S2, a knowledge consistency verification module: the knowledge consistency verification module makes the features of the input model highly reliable through a lightweight fact consistency detection process; S3, a thinking chain reasoning module: the thinking chain reasoning part performs task decomposition, and the complete reasoning chain from user preference analysis to final score generation; S4, a fusion module: the fusion module adjusts the adaptive weight, fuses the statistical results of the traditional recommendation channel and the reasoning results of the large language model channel, and generates the final recommendation result, can solve the illusion and deviation problem of the large language model, and enhance the recommendation performance by integrating the deep reasoning large language model.

An object of a system according to an embodiment is to automatically propose an optimal schedule that suits an individual's hobbies and preferences and register the schedule in a calendar.SOLUTION: A system according to an embodiment includes a preference analysis unit, an event search unit, a schedule proposal unit, a transportation registration unit, and a schedule selection unit. The hobby and preference analysis unit analyzes personal hobbies and preferences using the search history of the smartphone and other data. The event search unit searches for an event in consideration of weather and location. The schedule proposal unit automatically collects information on living places and various events and proposes an optimal schedule. The transportation registration unit registers transportation, a fee, and the like for participating in the proposed schedule in the calendar. The schedule selection unit allows the user to smoothly select a schedule that suits the user's hobbies and preferences from the proposed schedule candidates.SELECTED DRAWING: Figure 1

The application provides an artificial intelligence-based content recommendation method and device, an electronic device, and a computer readable storage medium. The method comprises: in response to a touch operation of a content presentation interface, obtaining historical operation behaviors of a user account logged into the content presentation interface; determining an account attribute of the user account according to the historical operation behaviors; obtaining account preference behaviors conforming to the account attribute, and performing preference analysis and processing according to the account preference behaviors to obtain preference information of the user account for multiple contents in a content library; determining a content recommendation strategy according to the account attribute and the preference information, and screening out to-be-recommended contents from the content library according to the content recommendation strategy; and updating the content presentation interface according to the screened out to-be-recommended contents. Through the application, accurate content recommendation can be achieved, and the actual utilization rate of the computing resources consumed by the electronic device when performing content recommendation can be improved.

An object of a system according to an embodiment is to propose an optimal travel plan on the basis of preferences and interests of a user and cause the user to experience the travel plan in a virtual space.SOLUTION: A system according to an embodiment includes a preference analysis unit, a travel plan suggestion unit, a virtual experience providing unit, and a shared experience unit. The preference analysis unit analyzes preferences and interests of the user. The travel plan suggestion unit suggests an optimal travel plan based on the information analyzed by the preference analysis unit. The virtual experience providing portion causes the user to experience the travel plan suggested by the travel plan suggestion portion in the virtual space. The shared experience unit shares the virtual travel experience provided by the virtual experience providing unit with other users.SELECTED DRAWING: Figure 1