570 results about "Click-through rate" patented technology

The invention discloses an accurate advertisement putting method and system based on artificial intelligence, and relates to the technical field of information, and the method comprises the steps: collecting user behavior data and real-time situation data, and carrying out the preprocessing of the user behavior data; analyzing the preprocessed user behavior data to generate a user interest label; combining the real-time situation data with the user interest label, constructing a situation interest model by adopting a deep learning technology, and predicting a user interest change mode; using an interest-driven recommendation method to obtain an initialized advertisement putting strategy; generating feedback information based on the analysis of the initialized advertisement putting strategy; and optimizing the feedback information through a reinforcement learning algorithm to generate an optimized advertisement display strategy. By vectorizing the user interest and combining the advertisement strategy characteristics, the generator model can meet the user preference and follow the advertisement materials of the optimal display strategy, so that the visual experience and the emotion resonance of the user are improved, and finally the click rate and the conversion rate of the advertisement are improved.

The invention discloses a video feature extraction and multi-dimensional matching-based movie advertisement real-time pushing system, and belongs to the technical field of digital advertisements. According to the system, multi-modal analysis is carried out on visual, audio, text and semantic features of a movie through a video feature extraction module, a dynamic interest tag constructed by a user portrait analysis module is combined, and the weighted matching degree of a video scene, user preference and an advertisement tag is calculated by utilizing a multi-dimensional matching engine; and millisecond-level advertisement putting is completed through the real-time pushing decision module. According to the method, context awareness and streaming computing technologies are fused, the relevance between advertisements and content scenes is remarkably improved, cross-platform deployment is supported, the method is suitable for short videos, live broadcast and other real-time scenes, the advertisement click rate is increased by 55%-72% through tests, the response delay is lower than 200 ms, and the method has the technical advantages of being efficient, accurate and low in delay.

The invention relates to the technical field of advertisement putting and intelligent decision making, in particular to a favorite advertisement putting system for predicting the advertisement click rate, which comprises a context awareness intelligent adaptation unit and a deep enhancement decision making unit. By means of deep semantic analysis and situational inference engine processing, interest keywords are extracted by constructing a specific model, user situational portraits are constructed in combination with multi-source data, a preliminary advertisement set is screened by matching with an advertisement material library, a multi-agent architecture is constructed by a deep reinforcement decision unit, a master agent performs overall planning, slave agents are responsible for different advertisement types, and a user can perform multi-agent interaction. A multi-dimensional reward function system is designed, each agent collects feedback data such as operation of a user on an advertisement page, a reward value is calculated according to the feedback data, a strategy network is updated, a main agent integrates information to optimize an overall advertisement pushing strategy, accurate pushing of advertisements is achieved, and the advertisement click rate and the putting effect are effectively improved.

The invention discloses a short video advertisement putting optimization evaluation method based on deep learning, and relates to the technical field of advertisement putting, and the method collects user behavior data in real time, constructs a dynamic user portrait, enables an advertisement system to adjust the advertisement content and putting strategy in time according to the interest change of a user, improves the correlation of advertisements, and improves the advertisement putting efficiency. Meanwhile, user behavior mode data and advertisement content labels are subjected to multi-dimensional matching, and the matching degree of the advertisements and user interests is calculated by combining cosine similarity with a weighting algorithm, so that advertisement recommendation is more personalized and accurate, the advertisement click rate and conversion rate are greatly improved, and the advertisement recommendation efficiency is improved. Meanwhile, the optimal opportunity of advertisement display is defined by analyzing the exit tendency of the user and the advertisement clicking tendency, it is guaranteed that the advertisement can be displayed at the moment when the user is most likely to interact and click, and the problem of low conversion rate caused by poor opportunity in a traditional method is solved.

The invention discloses a new media-oriented interactive marketing method, which comprises the following steps of: 1, multi-source data acquisition: acquiring behavior data of a user in social media, a short video platform and an e-commerce scene in real time through an API (Application Program Interface), a burying point technology and cross-platform data crawling, comprising click tracks, staying duration, comment content, sharing behaviors and shopping cart operation; through the technical innovation of a dynamic content engine, a real-time feedback closed loop, edge calculation, reinforcement learning, a multi-modal material library, a GAN automatic synthesis technology, federated learning, a block chain and the like, the core benefits of improving the interactive conversion efficiency, data-driven real-time decision, reducing the content generation cost, balancing the safety and the effect, performing scene-based accurate touch and the like are realized; the user conversion rate, the click rate, the marketing response speed, the negative comment processing efficiency, the portrait accuracy and the target user coverage rate are remarkably improved.

A system for the dynamic optimization of sponsored product advertising using real-time data and machine learning, which includes the following: a) a data collection and integration module configured to collect real-time data from advertising platforms, e-commerce websites and user interactions, and to integrate key performance indicators such as impressions, click-through rates (CTR), conversion rates and competitors' bidding strategies; b) a data preprocessing and filtering module configured to clean, normalize and structure the collected data by removing inconsistencies, duplicates and irrelevant information; c) a machine learning machine configured to analyze historical and real-time data using predictive models, including deep learning and regression algorithms, to identify patterns in user behavior and optimize bidding strategies; d) a real-time trend analysis module configured to detect emerging market trends, seasonal fluctuations and changes in consumer sentiment to enable adaptive adjustments to ad placement; e) a bid management and optimization module configured to dynamically adjust ad bids in real time based on auction data, competitor activity and performance metrics to ensure cost-effective ad placements; f) a module for targeting and segmenting audiences, configured to classify users based on demographic data, browsing behavior, purchase history and engagement patterns, thus ensuring optimal ad delivery to relevant consumers; (g) an ad creative optimization module configured to analyze ad performance using A / B testing and natural language processing (NLP) and automatically make recommendations or changes to ad creatives to improve engagement and conversion rates; h) a performance monitoring and analytics dashboard configured to provide real-time visual insights into ad performance metrics, optimization recommendations, and budget usage; (i) an automated decision and control system configured to make bid adjustments, budget reallocations, and target audience refinements based on machine learning insights without manual intervention; and j) a compliance and fraud detection module configured to monitor ad interactions, detect fraudulent activities such as click fraud and bot traffic, and ensure compliance with platform policies; with the system continuously optimizing sponsored product advertising by using real-time data and machine learning to maximize return on investment and improve advertising efficiency.

The invention discloses an advertisement recommendation method and system based on a click rate estimation model, and relates to the technical field of advertisement recommendation, and the method comprises the steps: collecting advertisement recommendation feature data, carrying out the processing through a feature project, and outputting a user feature vector; inputting the user interest vector into a click rate estimation model, training the click rate estimation model through a causal optimized cross entropy loss function, and outputting a click probability value of each advertisement; the click probability value serves as input of a reinforcement learning algorithm, and an advertisement sorting result is generated by dynamically optimizing a multi-target reward function in combination with user interaction behaviors; according to real-time feedback data in the user interaction behavior, updating a user interest partial value of the causal inference model in real time, and correcting user interest vector offset; by updating the user interest partial value in real time, the user behavior change can be quickly responded, and the timeliness of advertisement recommendation and the user experience are improved.

The invention provides a multi-platform internet marketing activity effect evaluation and prediction model construction method, and relates to the technical field of data analysis. The method comprises the following steps: collecting marketing activity data from a plurality of internet platforms; performing cleaning, normalization and feature engineering processing on the data; constructing a multi-level evaluation and prediction model based on a machine learning algorithm, wherein the multi-level evaluation and prediction model comprises a data fusion model, an effect evaluation model and an effect prediction model; calculating a click rate, a conversion rate, a user retention rate and a user life cycle value by using an effect evaluation model so as to comprehensively evaluate a marketing activity effect; and performing effect prediction by using the effect prediction model, and evaluating the model through a root-mean-square error, a mean absolute error and a mean absolute percentage error. According to the invention, the accuracy and comprehensiveness of marketing activity effect evaluation and prediction can be improved, and an important basis is provided for optimizing a marketing strategy.

The invention relates to a method and device for optimizing the click effect of an advertisement platform based on artificial intelligence, and belongs to the technical field of advertisement platform click effect optimization, and the method comprises the steps: carrying out the feature extraction of an image advertisement material and a text advertisement material through a feature extraction algorithm, and obtaining an image feature vector and a text feature vector; the classification algorithm classifies the image advertisement material and the text advertisement material according to the image feature vector and the text feature vector to obtain a classification result; acquiring historical behavior data of the user on the advertisement materials, and analyzing preferences of the user on different types of advertisement materials through a deep learning algorithm to generate a user preference model; acquiring real-time behavior data, and adjusting the display frequency and sequence of the advertisement materials according to the real-time behavior data and the user preference model; and adjusting the advertisement material display frequency, display position and material content according to the user preference model and the classification result. According to the method, the advertisement material click rate, the personalized recommendation effect and the advertisement material putting efficiency are remarkably improved.

The embodiments of the present application relate to the technical field of artificial intelligence, and provide an information recommendation method and apparatus, a device, and a storage medium. The method comprises: performing training on the basis of historical user material interaction information to obtain a deep semantic recall model and a deep semantic sorting model; and by means of the deep semantic recall model, recalling a plurality of candidate materials corresponding to a user identifier, and by means of the deep semantic sorting model, determining and sorting preference scores of the plurality of candidate materials and recalled materials of other paths, so as to recommend materials, thereby eliminating dependence on operation experience, and improving the recommendation effect. The training enables the deep semantic recall model to learn the text semantic representation of the user material interaction information, so that when materials are recalled, the literal semantics of the materials is more concerned, thereby improving the accuracy of the recalled materials. The deep semantic sorting model is enabled to learn the behavior semantic representation of the user material interaction information, so that when the recalled materials are subjected to preference scoring and sorting, the user behavior information of the material feedback level is more concerned, thereby improving the user click rate.

The present invention relates to the field of intelligent marketing technology, specifically to a SAAS platform based on mobile digital marketing and its method, wherein the platform includes a behavior sequence module, an identity matching module, a conversion willingness evaluation module, a marketing link optimization module, and a reach strategy adjustment module. In the present invention, through the timestamp sorting and behavior pattern analysis of access data, it is possible to accurately identify the access stability between devices, effectively eliminate random interference, ensure the accuracy of identity recognition, classify unpaid behaviors, optimize marketing strategies, screen user interest preferences, match suitable marketing reach strategies in an automated manner, realize personalized push, improve user interaction rate, adjust push content and frequency, make marketing information more targeted, reduce user interference, improve click-through rate and stay time, improve user conversion rate, and optimize marketing links through a closed data loop to form a more efficient marketing strategy execution system.

The invention discloses an intelligent advertisement optimized delivery system based on user behavior analysis, and the system comprises a data collection and preprocessing module which is used for collecting user behavior data, and carrying out the cleaning, analysis and processing of the user behavior data, so as to obtain the effective behavior data of a user; the user portrait construction module is used for constructing a user portrait by utilizing the effective behavior data of the user; the advertisement feature extraction module is used for performing multi-modal feature extraction on the advertisement materials to construct semantic representation of the advertisement; the conversion estimation model construction module is used for constructing a conversion estimation model through a training deep learning algorithm by utilizing the effective behavior data of the user and the advertisement features; the intelligent delivery decision module is used for constructing an intelligent delivery decision model; and an effect verification and optimization module. Through the scheme, user interests and advertisement contents can be accurately matched, advertisement sorting is optimized, the click rate and the conversion rate of advertisements are improved, meanwhile, invalid advertisement exposure is reduced, and the rate of return on investment of advertisers is improved.

The invention belongs to the technical field of catering informatization service, and provides an automatic matching catering menu pushing system, which comprises a user data acquisition module, a catering menu pushing module and a catering menu pushing module, the menu data management module stores and manages menu classification, labels and states, and obtains content feature data; the user portrait construction module generates a multi-dimensional portrait according to the multi-aspect information of the user; the menu matching module combines collaborative filtering and a content recommendation algorithm, and generates a recommendation menu based on the user portrait; the intelligent pushing module pushes dishes of nearby merchants according to the portraits, and the pushing effect is evaluated through the click rate and the conversion rate; according to the method, personalized and accurate catering menu recommendation is realized, and real-time updating and optimization are carried out in combination with geographic positions and time period factors, so that personalized pushing which better fits user preferences and dining environments is provided.

The invention discloses a precise advertisement putting optimization method and system, and relates to the technical field of advertisement putting, and the method comprises the following steps: collecting and recording key parameter information of different data sources in a merging process in real time, providing basic data for the subsequent anomaly detection, and providing an optimization result for guaranteeing the comprehensiveness of the data; recording each step of operation in all merging processes; and carrying out anomaly analysis on the key parameter information subjected to the standardized processing, and identifying abnormal data generated in the merging process by analyzing multi-dimensional information. According to the invention, by monitoring the cross-session persistence information and the platform situation consistency information in real time, the system can effectively prevent the problem of data merging errors or segmentation, improve the accuracy of user portraits, and accurately push advertisement contents, thereby improving the click rate and the conversion rate. A multi-level anomaly detection and grading processing mechanism ensures that risks are timely handled, the safety of data integration and the stability of advertisement putting are improved, and the data integrity is guaranteed.

The invention relates to the technical field of electronic shopping, in particular to a big data analysis method based on an e-commerce platform and an AI system, and the method comprises the following steps: collecting a click rate, a search frequency and a purchase record of a user on the e-commerce platform, carrying out the subdivision of the click rate, the search frequency and the purchase record, and creating a data set; according to the invention, by integrating the equipment fingerprint technology and the social media data, the analysis precision of the user behavior data is improved, and the capabilities of user identity recognition and behavior pattern analysis are further optimized. In-depth analysis is carried out on user interaction data such as click rate, search frequency and purchase records, consumer behavior patterns and preference prediction are refined through clustering and decision tree algorithms, and sentiment analysis and keyword extraction are carried out on social media posting data. Therefore, the enterprise can monitor the market dynamic state in real time and quickly adjust the marketing strategy. And the accuracy of market prediction and the pertinence of marketing activities are improved.

The invention discloses an AI intelligence-based advertisement analysis system and method, and particularly relates to the technical field of advertisement analysis, and the method comprises the steps: obtaining a first period of putting an advertisement on a target APP, dividing the first period into a plurality of sub-periods, and extracting the advertisement click rate of each sub-period; determining data points of each sub-cycle to obtain a data set according to the number of the newly added users and the number of the purchase users, clustering the data set to determine an advertisement putting effect index of each sub-cycle, drawing a time-advertisement putting effect broken line graph, and dividing a trend state of the advertisement putting effect of a first cycle according to the broken line graph, and when the trend state of the first period is the rising trend, the duration and the putting frequency of the advertisement in the next putting period of the target APP are increased, so that the advertisement putting frequency and the duration of the putting period can be flexibly changed according to the actual advertisement putting effect in the advertisement putting process, the budget waste is reduced, and the advertisement putting efficiency is improved. And meanwhile, the maximization of the advertising effect is realized.

The invention relates to the field of movies, and discloses a movie recommendation method and system based on big data, and the method comprises the steps: collecting the multi-dimensional behavior data of a user to form an initial behavior data set; based on the initial behavior data set, calculating preference scores of the user for different categories of short play contents to obtain a user interest distribution matrix; dividing user groups and labeling user category labels; forming an initial short episode content feature set; constructing a content semantic feature matrix; if the content semantic feature matrix and the user category label have the matching requirement, judging the integrating degree of the short episode content and the user preference, and outputting a preliminarily recommended short episode content list; and on the basis of the preliminarily recommended episode content list, sorting and optimizing the recommended episode content by using click rate and complete playing rate indexes in the historical distribution data to obtain a final recommended episode content list. The method has the following effect that the accuracy and the individuation degree of short play recommendation are improved.

The invention relates to the technical field of vending cabinet advertisement management, in particular to an intelligent vending cabinet commodity advertisement recommendation management system. By calculating the variance and entropy of a behavior feature set and an environment feature set and describing the complexity of behavior distribution and the complexity of the environment, the dimension of a projection weight matrix and the dimension of a key are dynamically adjusted, by optimizing the dimension of the key, the dimension of the model can be adjusted according to the complexity of the environment and behavior data, the precision of the model in multi-modal data fusion is improved, and the multi-modal data fusion efficiency is improved. The personalized degree of advertisement recommendation is improved; through a Gaussian process modeling click rate and a Bayesian optimization algorithm, effective exploration is carried out in advertisement recommendation, so that a vending cabinet can continuously adjust a recommendation strategy according to an actual effect of an advertisement, and in combination with a multi-arm bandit algorithm, an optimal display strategy is searched between a known good-performance advertisement and an advertisement with an unknown potential effect. And the accuracy and efficiency of advertisement recommendation are gradually improved, so that the optimization of recommended advertisements is realized.

A chatbot system detects commercial intent during conversations with users and provides targeted advertisements. The chatbot system extracts keyword candidates from a conversation and assigns relevance scores based on the meaning of the conversation and commercial scores based on a machine learning model trained to detect commercially-related keywords. The chatbot system selects keywords based on a combination of the scores and transmits the selected keywords to advertising content servers that provide advertising content including advertisements selected using the keywords. The chatbot system displays the advertisements to the user during the conversation. The chatbot system may integrate the advertisements into the chatbot's responses, select advertisements based on a predicted click-through rate for the user, train the machine learning model using translations of labeled examples, access a long-term memory of the user's conversations and user profile data to provide context for the advertisements, and analyze messages from multiple users in group chats.

The invention relates to the technical field of business propaganda and pushing, in particular to a telecommunication business propaganda and pushing method based on artificial intelligence. Comprising the following steps: S1, collecting behavior data, preference data and telecommunication service data of a user, and sending the collected data to a database for storage; s2, obtaining a push value of a telecommunication service product through multi-dimensional analysis of the user information and the telecommunication service product information; s3, performing comprehensive analysis on the user active time, the login frequency and the response behavior to realize accurate pushing of related telecommunication services; s4, obtaining real-time feedback of the user to the pushed content by providing a user feedback entrance; through analysis of the time period and the activity degree, pushing is carried out at the active time of the user, the opening rate and the click rate of pushing are improved, the pushing frequency and the pushing interval are adjusted, and excessive interference to the user is avoided; the invalid pushing frequency is reduced, pushing in a low-response-rate channel and an inactive time period is avoided, and resource use is optimized.

The invention relates to the technical field of advertisement pushing, in particular to an intelligent pushing strategy method and system for real-time user behavior monitoring. The method comprises the following steps: acquiring user behavior data; performing feature extraction according to the user behavior data to obtain user behavior feature data; performing user behavior change capture according to the user behavior feature data to obtain user behavior change data, and performing model updating according to the user behavior change data and a preset user interest model to obtain a user real-time interest model; and generating advertisement content according to the user behavior data and the user real-time interest model to obtain user real-time advertisement content data so as to carry out real-time user behavior advertisement pushing operation. According to the method, the pushed content is optimized, so that the pushed content is highly matched with user requirements, interests and environments, and the click rate and the conversion rate of pushing are remarkably improved.

The invention relates to a fusion sorting recommendation method and system based on multi-path recall, and belongs to the field of recommendation systems. Comprising the steps of obtaining commodity information and an interaction behavior between a user and a commodity; calculating a first recommendation score of each commodity through a content-based recall mode on the basis of the similarity between the commodity attribute features and the user portraits; calculating a second recommendation score of each commodity through a recall mode based on collaborative filtering based on the user behavior characteristics; based on click rates of different recall modes, performing weighted average on the first recommendation score and the second recommendation score, and calculating a weighted average score of each commodity; sorting the commodities in a descending order according to the weighted average score to obtain a commodity recommendation list; and preprocessing the commodity recommendation list to generate a final recommendation list. And the correlation, diversity and effectiveness of recommendation results are improved.

The invention relates to the technical field of user personalized content pushing, in particular to a user personalized content pushing system based on big data and a promotion method. The method comprises the following steps: collecting user behavior and preference data through multiple channels; dynamically constructing user portraits, and enriching the user portraits in combination with multi-dimensional data; based on user portraits and content features, a recommendation model is constructed by using a deep neural network, and user preferences are accurately predicted through multiple hidden layers; through a push model and a multi-objective optimization method, the probability of pushed content is calculated in combination with indexes such as a click rate and a conversion rate, the push frequency and content selection are optimized, and the correlation of the pushed content and the user satisfaction are ensured; by monitoring user feedback in real time and adjusting a pushing strategy, intelligent decision and personalized recommendation of content pushing are realized, and long-term accurate pushing and improvement of user experience are ensured. According to the method, the accurate user portrait is constructed through multi-data source collection and processing, so that personalized content pushing is realized.

The invention provides a cross-modal collaborative recommendation method and system based on a large language model. The method comprises the steps of obtaining identity information of a user and a to-be-recommended item set; inputting the identity information and the item set into a preset recommendation model, so that the recommendation model predicts the click rate of each item in the item set according to the identity information, and outputting an item recommendation list according to the click rate of each item; wherein the recommendation model is constructed on the basis of a large language model and is obtained through training by using a training data set, and the training data set is based on the large language model and a collaborative filtering technology. And the text data of the recommendation fields and the user-project interaction data are constructed after prompt sequence conversion, feature mapping and feature fusion, so that the project recommendation accuracy of the large language model is improved.

The invention relates to the technical field of advertisement putting methods, in particular to an information flow advertisement module putting method, which specifically comprises the following steps of: 1, constructing a user portrait, collecting and analyzing behavior data of a user on various platforms, and constructing the user portrait; 2, analyzing and classifying advertisement materials; step 3, selecting and evaluating a delivery platform, namely selecting a corresponding delivery platform and evaluating the delivery platform; step 4, making a putting strategy, and making a specific putting strategy according to a user portrait, advertisement material classification and a putting platform evaluation result; 5, real-time monitoring and data collection: in the advertisement putting process, monitoring the advertisement putting effect in real time and collecting related data; according to the method and the device, the advertisement putting accuracy can be improved, an advertiser can accurately reach a target customer group, and the click rate and the conversion rate are improved.

The present application provides a click-through rate estimation method and apparatus, an electronic device, a storage medium, and a program product. The method comprises: on the basis of a user feature vector and an advertisement feature vector, determining a first attention weight between a user feature and an advertisement feature; on the basis of a first vector determined by the first attention weight, encoding the user feature, and obtaining a second vector; on the basis of the second vector, the user feature vector, and the advertisement feature vector, determining a second attention weight; on the basis of the second vector and a third vector determined by the second attention weight, obtaining a splicing vector; and on the basis of the splicing vector, obtaining a click-through rate estimation result of an advertisement. By means of the present application, the click-through rate of a user for an advertisement can be predicted more accurately.

The invention belongs to the technical field of electronic commerce, and particularly relates to a long-tail commodity recommendation method based on precise symmetric positive definite manifold learning. Comprising the steps that user behavior data and commodity feature data of an e-commerce platform are acquired and preprocessed, and a commodity feature matrix and a user commodity interaction matrix are obtained; optimizing the user commodity interaction matrix by adopting a low-rank approximation technology, and updating the user commodity interaction matrix; mapping the commodity feature matrix into a symmetric positive definite matrix space, and performing manifold embedding processing to obtain an SPD feature vector of the commodity; carrying out aggregation processing on the SPD feature vectors of the commodities by adopting a popular full-connection layer to obtain aggregation features; mapping the aggregated features to Euclidean space by using a geometric preserving mapping function to obtain mapping features; processing the mapping features to obtain a long-tail commodity recommendation result; according to the method, the advertisement click rate of the e-commerce platform is improved, and the precision marketing and recommendation effects are remarkably enhanced.

The invention provides a search engine advertisement display method and a related device, which are applied to terminal equipment or a server, and the method comprises the following steps: determining the correlation between search content input by a user and one or more groups of attribute information, the one or more groups of attribute information corresponding to a first advertisement, each group of attribute information comprises at least one of the following items: a field related to the first advertisement, a descriptive statement for the first advertisement or a keyword of the first advertisement, and the first advertisement is an advertisement to be displayed determined based on the search content; and determining a target text for display based on the correlation, wherein the target text is a language description of the first advertisement. In the embodiment of the invention, the target text based on the advertisement can enable the user to quickly know that the pushed advertisement is related to the search content input by the user, so that the user feels that the pushed advertisement is possibly helpful to the user, the user can be better attracted, and the click rate of the pushed advertisement can be improved.

The invention discloses an advertisement creative real-time optimization and putting control method and system based on artificial intelligence, electronic equipment and a computer readable medium, and the method achieves the precise matching, effect prediction and dynamic regulation and control of the advertisement creative through an intelligent technology, thereby improving the efficiency and effect of advertisement putting. The core process of the method comprises the following steps: firstly, carrying out fusion and joint modeling on multi-source features such as user portraits, advertisement creative contents and real-time context environments so as to realize high-matching-degree real-time optimization of creativity, users and scenes; and secondly, a deep cross network (DCN) model is adopted to capture a complex high-order interaction relationship among the user, the creativity and the environment, so that key indexes such as a click rate (CTR) and a conversion rate (CVR) are predicted with high precision. Besides, the system establishes a real-time monitoring mechanism based on a sliding time window and a change slope, creative fatigue is actively identified through the change trend of data such as exposure, clicking and conversion, and intervention and adjustment are carried out in time.

The invention discloses a new media promotion method and system based on the Internet, and belongs to the technical field of new media promotion, and the method specifically comprises the steps that the distribution density and the stop position of people in a waiting area are monitored in real time, load distribution data in a lift car are collected, and the vibration frequency and the start-stop state in the elevator running process are recorded; when it is detected that the density of people in the waiting area exceeds a set threshold value, switching to duration compressed advertisement content, and when the load distribution of the lift car displays that one side is crowded, displaying interactive advertisements in the free area; capturing a user eyeball movement track, calculating an advertisement interface fixation thermodynamic diagram, identifying a user gesture operation by using a millimeter wave radar, mapping a specific gesture into an advertisement content switching instruction, and generating a personalized advertisement recommendation scheme; and the advertisement interaction data is converted into a pushing strategy adjustment basis of adjacent elevators, a propagation path is established according to the flow rule of people in the elevator room, and when the advertisement click rate of a certain elevator exceeds a set value, a content recommendation signal is sent to the associated elevator.