852 results about "Ad serving" patented technology

The invention discloses an artificial intelligence-based advertisement effect evaluation method and system, and the method comprises the steps: synchronously obtaining multi-source data containing a user behavior data flow and an advertisement putting index flow through a distributed collection engine, and generating a time-space synchronous multi-dimensional data cube; performing feature decoupling on the multi-dimensional data cube, and outputting a dynamic feature topology network with a weight; inputting the dynamic feature topology network into an adversarial training framework, and finally outputting an advertisement conversion probability space-time distribution diagram; based on the advertisement conversion probability space-time distribution map, deploying an attribution calculation unit for real-time feedback, and generating an incremental attribution map with a confidence interval; and inputting the incremental attribution atlas into a strategy generation adversarial network, and outputting an adversarial optimization advertisement putting strategy set meeting Pareto optimum. According to the embodiment of the invention, the accuracy and real-time performance of advertisement effect evaluation can be improved.

The invention relates to a data processing method and device based on big data and advertisement pushing, and the method comprises the following steps: obtaining the historical behavior data of a user on a multi-channel platform, constructing a dynamic interest label map according to the historical behavior data, and depicting a user interest evolution process. Combining with a social relation network to analyze an interest propagation path, forming a user social interest diffusion trajectory, and introducing a time decay weighting mechanism to generate a dynamic interest decay curve. According to the method, user interests and advertisement materials are subjected to semantic similarity matching, a personalized advertisement recommendation list is generated, an optimal advertisement putting strategy is determined through multi-target optimization configuration and comprehensive consideration of display positions, opportunities and forms, accurate and efficient advertisement pushing is achieved, and the problems that a traditional user portrait method often depends on a static label system, and the user experience is poor are solved. The dynamic characteristic that the user interest changes along with time is difficult to reflect, so that the advertisement recommendation content lags behind the real intention of the user.

The invention discloses an intelligent recommendation method for optimizing advertisement keyword combination through cross validation, and relates to the technical field of advertisement technology and search engine marketing, which comprises the following steps: constructing a heterogeneous data set through multi-modal data fusion, and layering according to data sparseness: training a Transform-XL time sequence model by adopting time cross validation of a dynamic K value in a high resource layer; a graph neural network association graph is introduced into a low resource layer, semantic expression of a long tail word is enhanced, a stratified sampling-transfer learning two-channel mechanism is designed, and the generalization ability is improved in combination with exposure frequency weighting and a parameter freezing strategy; developing a Bayesian fusion engine, and dynamically weighting a high / low resource layer prediction result by using an improved Materon kernel function Gaussian process; and generating a confidence interval based on neural quantile regression, and outputting an optimal keyword combination sequence under ROI-risk-diversity constraint in combination with multi-target Pareto optimization. According to the method, the cold start efficiency and the long-tail resource utilization rate are improved, and high-robustness decision support is provided for advertisement putting.

The invention relates to an intelligent delivery decision-making method and system based on multi-dimensional index association, and the method comprises the following steps: S1, collecting user, advertisement and context multi-dimensional data, and carrying out the preprocessing of the data to generate standardized features; s2, performing fusion calculation on the multi-dimensional standardized features, extracting key features, and constructing and generating a user-advertisement-context joint feature set; s3, constructing a multi-task prediction model, and training according to a user-advertisement-context joint feature set; s4, according to a prediction result and real-time features of the trained multi-task prediction model, rapidly matching an optimal advertisement for a given user in a real-time bidding process; and S5, performing causal analysis according to the exposure / click log of the optimal advertisement, verifying the real effect of the advertisement, correcting the index, and feeding back to the feature engineering in the S2 and the model training step in the S3 according to the corrected index. According to the invention, the advertisement putting efficiency and effect are effectively improved.

The invention discloses a real-time behavior monitoring and dynamic optimization system for an advertisement putting strategy, and relates to the technical field of advertisement putting and user behavior analysis. A real-time multi-source user behavior data acquisition module acquires data; the distributed storage and indexing module supports fragmented storage, capacity expansion and multi-dimensional indexing; the behavior feature extraction and analysis module extracts features and identifies a time sequence rule; the putting effect evaluation module compares data through an index system and positions an effect short board; the user portrait dynamic updating module adjusts the label weight according to the latest data; the strategy optimization decision module optimizes parameters in combination with the portrait and the evaluation result; the execution scheduling module issues an instruction and monitors a state; the historical data mining and model training module is used for mining rules and iteratively training the model; and the visual interaction configuration module provides an interface and supports parameter configuration and data, effect and optimization suggestion display. According to the invention, full-scene real-time monitoring and cross-platform identity association of user behaviors are realized, and support is provided for refined advertisement marketing.

The invention belongs to the field of advertisement putting, and provides an advertisement putting method and system based on multi-source data analysis, and the method comprises the steps: collecting multi-source original data related to a user; identifying a plurality of cognitive state nodes based on the click behavior data and the transaction path data; constructing a cognitive behavior causal atlas based on the plurality of cognitive state nodes, wherein nodes of the causal atlas represent user cognitive states; predicting an advertisement response probability and a conversion probability of a target user by adopting a Bayesian inference model in combination with a historical behavior sample and a path structure in the causal atlas, and estimating a state transition probability of the user from a current state node to a target state node based on different advertisement intervention contents; and based on the state transition probability and the causal atlas structure, determining an optimal advertisement intervention path of the user from the current cognitive state to the expected conversion state, and constructing a corresponding advertisement putting sequence based on the path.

The invention discloses an AI-based intelligent marketing advertisement putting method and system, and the method comprises the steps: collecting and fusing heterogeneous user behavior data streams from a plurality of independent platforms in real time, and generating a user behavior feature map with a unified space-time mark; on the basis of the user behavior characteristic spectrum, a deep auto-encoder and a space-time diagram convolutional network are used for joint modeling, and fine-grained scene-decoupled user interest preference vectors are generated; inputting the user interest preference vector into a pre-trained generative adversarial network, and dynamically generating a personalized advertisement material highly matched with the current user interest preference and scene; and taking the personalized advertisement material as a candidate arm, and dynamically generating an advertisement putting strategy including an advertisement display form, a display opportunity and a display channel in combination with a current user state and historical putting feedback data. By utilizing the embodiment of the invention, the accuracy and the real-time performance of advertisement putting can be improved, and the advertisement conversion rate and the user experience are improved.

The invention relates to the technical field of Internet advertisement putting, in particular to an intelligent huge advertisement putting method, which comprises the following steps of: acquiring and preprocessing multi-dimensional operation data in real time, constructing a user response prediction model based on a deep interest network, and dynamically weighting a user behavior sequence by utilizing an attention mechanism. A dynamic budget allocation optimization model is established; an objective function is set according to the type of an advertiser; a budget weight is adjusted according to a real-time bidding success rate; a real-time decision engine is deployed; according to the method, each link is monitored through an exposure conversion funnel, an abnormal alarm is set, strategy backtracking analysis is started, and continuous optimization is performed in combination with online learning and model gray release, so that the problem of low efficiency of budget allocation in the traditional technology is solved, intelligent and accurate putting of a huge amount of advertisements is realized, the advertisement conversion rate is improved, and the customer obtaining cost is reduced.

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 provides a visual communication advertisement design system and method, and the method comprises the steps: carrying out the cross-modal feature alignment of a background voiceprint feature extracted from environment voiceprint data of an advertisement putting scene and a frequency domain feature of illumination intensity in illumination intensity data, and obtaining an environment perception state tensor; generating a dynamic illumination matrix by combining the spectral irradiance distribution in the environmental perception state tensor with the anisotropic highlight coefficient of each sub-region in the target advertisement picture; determining an audience space domain according to the space depth data of the advertisement putting scene and the audience motion trail, and performing space audio redirection on the target advertisement based on the audience space domain and the sound field energy distribution in the environment perception state tensor to obtain an acoustic beam width angle; and performing acousto-optic synchronous dynamic rendering on the advertisement content of the target advertisement according to the dynamic illumination matrix and the acoustic beam width angle. By adopting the scheme of the invention, acousto-optic collaborative rendering and acousto-optic parameter dynamic adaptation can be performed on the target advertisement so as to realize directional propagation to audiences.

The invention discloses a multi-channel advertisement effect prediction and optimization method and system based on artificial intelligence. The method comprises the following steps: obtaining and preprocessing advertisement putting data, user behavior data and external environment data; performing feature extraction on the preprocessed data, mapping different types of content feature vectors to a unified semantic space through a cross-channel feature projection matrix, and generating an advertisement feature set; fusing a prediction model of Transform and a graph neural network, and synchronously predicting a conversion rate, a delivery return rate and a user interaction index of the target advertisement in each channel based on the advertisement feature set; constructing a strategy optimization network based on reinforcement learning, taking a prediction result output by the prediction model as state input, and dynamically generating a budget allocation matrix and a channel selection weight through a strategy gradient algorithm; and pushing the adjusted advertisement putting strategy to an advertisement management platform according to the optimization strategy, and executing advertisement putting. The accuracy and stability of advertisement effect prediction can be improved.

The invention relates to the field of artificial intelligence, discloses a crowd advertisement accurate putting determination method based on a knowledge graph, and aims to solve the problem of mismatching caused by interest model lag in traditional advertisement putting. The method comprises the following steps: constructing a dynamic evolution type user interest knowledge graph, fusing multi-source behavior flow and static portrait data, and introducing a time decay factor and an event triggering mechanism to realize node weight self-adaptive updating; a short-term interest pulse is accurately captured through a time sequence attention propagation and cross-domain association edge dynamic generation algorithm; and constructing a dual-channel graph neural network inference engine, respectively processing long-term stable and short-term sudden interest paths, and generating a delivery decision through confidence weighted fusion. According to the invention, the timeliness and accuracy of advertisement matching are improved, the mismatching rate is reduced, and the user experience and the self-calibration capability are enhanced.

An adaptive dependency replay control system for use in a latency-sensitive ad serving backend, comprising: a microcontroller-based replay control engine configured to receive ad serving requests and interface with a variety of downstream microservices; a latency monitoring unit operatively coupled to the microcontroller, the latency monitoring unit continuously sampling and maintaining real-time latency histograms for each downstream microservice over sliding time windows; a health status aggregator communicatively coupled to the retry control engine, the aggregator configured to receive service-level health indicators, including, but not limited to, HTTP status codes, circuit breaker states, request timeout counters, and error rate thresholds; a retry decision processing unit stored in a memory accessible to the microcontroller, wherein the matrix can generate a retry action vector based on one or more of the following factors: dependency health, request priority, estimated ad impression value, and system resource metrics; a policy execution engine configured to evaluate retry policies expressed in a domain-specific retry policy language, wherein the execution engine resolves the policies into bytecode rules that are executed by the retry control engine in real time on a per-request basis; and at least one fallback path generator capable of returning an approximate or synthetic response instead of retrying a degraded dependency, where the fallback path is selected based on runtime evaluation of the policy conditions and a calculated retry confidence value.

The invention relates to the technical field of advertisement effect evaluation, in particular to a cross-screen interactive advertisement effect evaluation system based on multi-modal agent driving, which comprises the steps of collecting advertisement playing data of an advertisement on target equipment, obtaining a login account and a geographic position of the target equipment, performing account verification and distance verification within advertisement putting time, and obtaining the advertisement playing data of the target equipment. Identifying to obtain a cross-screen associated device of the target device; monitoring the running state of the cross-screen associated equipment to obtain user behavior data; constructing a user cross-screen behavior link according to the time sequence of the user behavior data; performing analysis based on the advertisement playing data of the target equipment and the user cross-screen behavior link of the cross-screen associated equipment to obtain cross-screen associated behavior data; and performing user conversion analysis of different advertisement space types based on the cross-screen association behavior data, and calculating an advertisement cross-screen interaction index. According to the method and the device, the advertisement effect is accurately evaluated by identifying the cross-screen behavior of the user.

The invention provides an elevator multi-mode crowd feature perception and intelligent advertisement putting method and system, and relates to the technical field of intelligent advertisement putting, and the method comprises the steps: collecting multi-mode perception data in an elevator through an edge computing terminal; performing feature decoupling on the data, performing cross-modal semantic alignment, establishing a directed association relationship between modals, and constructing a scene feature map; calculating the topology importance degree of map nodes, screening feature nodes, and extracting context information for semantic coding; mapping the scene semantic code and the advertisement audience semantic code to a two-dimensional coordinate system to construct a semantic matching graph, and extracting an optimal matching path to form a candidate set; predicting a scene evolution trend based on the scene characteristic spectrum evolution trajectory, and calculating an advertisement adaptive score to generate a playing sequence; putting and collecting user interaction data feedback according to the sequence to update the graph structure. According to the invention, accurate crowd feature recognition and advertisement dynamic matching are realized, and the advertisement putting efficiency and the user experience are improved.

The invention belongs to the field of advertisement analysis, and provides an advertisement putting effect evaluation and analysis system and method based on data analysis, and the method comprises the steps: constructing a cognitive state vector of a user based on multi-source behavior data before and after the contact between the user and an advertisement; modeling is conducted on user cognitive state vector changes caused by advertisement putting, cognitive intervention vectors of the advertisements are generated, and the cognitive intervention vectors represent the variable quantity of the cognitive states of the users after the users make contact with the advertisements; based on the language feedback, the visual behavior and the emotion track of the user, evaluating the consistency between the user behavior response and the cognitive intervention vector direction, and outputting a multi-modal behavior consistency score; tracking a behavior path of the user from advertisement click to final conversion, forming a conversion chain feature, and identifying a path length, a path node type and a path response time delay; and fusing the cognitive intervention vector, the multi-modal behavior consistency score and the conversion chain feature, and calculating a cognitive effect comprehensive score of advertisement putting for putting effect evaluation.

The invention relates to the technical field of cross-border advertisement putting, and provides a cross-border e-commerce advertisement accurate putting method and system based on a user portrait, and the method comprises the steps: building a unified data flow through multi-source data collection and standardization processing; calculating and fusing the real-time statistical features, the historical interest vectors and the context features in real time by using a stream processing engine to generate multi-dimensional feature vectors; inputting the feature vector into a multi-modal time sequence neural network model which captures a user behavior rule through time sequence coding and an attention mechanism and outputs a user behavior weight and an interest attenuation coefficient; dynamically updating the real-time interest score of the user by adopting an exponential decay model; and when the interest score reaches a threshold value, triggering a reinforcement learning agent to make a decision according to the comprehensive state information, and dynamically outputting an action strategy including release triggering, a bidding coefficient and a creative type. The problems that in the prior art, user interest modeling lags behind, and the self-adaptive capacity of the putting strategy is poor are effectively solved.

The invention provides an advertisement putting dynamic game decision-making method and system based on multi-objective optimization, and the method comprises the steps: obtaining user behavior key data which comprises user visual focus position information; transmitting the user visual focus position information to a multi-target optimization model in a preset decision server, and generating multi-target optimization model input data; analyzing the input data of the multi-target optimization model and a preset game constraint condition by using the multi-target optimization model to generate a multi-target game decision parameter; and carrying out dynamic game deduction calculation based on the multi-target game decision parameters, and generating a dynamic advertisement position adjustment scheme so as to carry out advertisement putting dynamic game decision. According to the method, millisecond data is transmitted to the multi-target optimization model, the problem of strategy oscillation caused by data delay and target conflict in traditional advertisement decision making in a real-time attention scene is solved, and accurate dynamic matching of user focus drift and advertisement display is realized.

The invention provides an advertisement putting strategy generation system based on artificial intelligence, and belongs to the technical field of artificial intelligence, and the system comprises a vector construction module which is used for collecting multi-source data of advertisement putting to be played, carrying out the feature extraction of the multi-source data according to dimension indexes, and constructing a feature vector of each dimension index; the strategy determination module is used for comprehensively analyzing the feature vectors under all dimension indexes according to the putting target of the to-be-put advertisement, and determining a plurality of candidate putting strategies in combination with the own playing strategy of the to-be-played advertisement; the effect evaluation module is used for performing pre-simulation delivery evaluation on each candidate delivery strategy, predicting the delivery effect of each candidate delivery strategy, determining strategy adjustment parameters according to the delivery effect, and screening an optimal delivery strategy; and the updating module is used for dynamically updating and re-putting the optimal putting strategy in combination with the newly collected multi-source data. And the putting pertinence, the effect stability and the environmental adaptability are obviously improved.

The invention discloses an advertisement interaction degree evaluation method and system based on user behaviors, and the method specifically comprises the steps: enabling a system to collect key behavior indexes of a user in advertisement interaction in real time according to presetting, enabling the key behavior indexes to serve as user interaction behavior data, and building a user interaction portrait through a machine learning algorithm based on the user interaction behavior data; according to an interaction degree scoring mechanism, different weights are given according to different interaction behaviors, the weights comprise high weight behaviors, middle weight behaviors and low weight behaviors, and interaction degree scores of corresponding advertisements are generated; further mining the user interaction behavior data by adopting a clustering analysis algorithm, and identifying a potential user behavior mode; performing correlation analysis on the interaction degree score of the advertisement and actual data of the advertisement, and generating a prediction rate rank of user interaction behaviors; and according to the real-time feedback of the advertisement interaction degree, the advertisement putting platform adaptively adjusts an advertisement strategy.

The invention discloses a data processing method and system based on a business management platform, particularly relates to the technical field of advertisement putting business management, and is used for solving the problems of difficulty in abnormal attribution and low decision-making efficiency caused by dimension data isolation of an existing system. The method comprises the following steps: synchronously acquiring a channel dimension click rate and a contact dimension blacklist interception rate; when the channel click rate is abnormally reduced, extracting a blacklist interception log associated with the corresponding channel and a product dimension conversion rate; user behavior path topology distortion intensity and decision manifold compactness caused by a blacklist strategy are verified based on an interception log; when the distortion intensity exceeds a threshold value or the compactness is lower than a threshold value, backtracking an advertisement exposure user path of a corresponding channel product; analyzing time sequence association of blacklist interception nodes and product exposure nodes in the path, and generating a candidate root cause set; and detecting the product conversion rate fluctuation direction in the abnormal time period, thereby determining the root cause from the candidate set, and remarkably improving the operation decision-making efficiency and accuracy.

The invention discloses an advertisement putting platform based on AI dynamic creative optimization, and relates to the technical field of advertisement putting, and the platform comprises five modules: a dynamic creative basic network construction module deploys multi-modal nodes to construct a full-scene generation network; the creative reference index acquisition module acquires effect and adaptive data, and analyzes to obtain a quantitative index; the creative failure evaluation module evaluates whether the effect of the creative element is attenuated according to the index; the alternative creativity planning module analyzes available alternative elements and plans a scheme; and the creative emergency response scheme execution module executes the emergency scheme when the alternative elements are all attenuated. The effect data comprises a click conversion rate and the like, and the adaptive data comprises theme correlation degree and the like, and calculating indexes through normalization processing. Attenuation judgment refers to effect index decreasing amplitude and adaptive index fluctuation, alternative schemes are screened according to priorities, emergency schemes are rapidly generated, new creativity is tested, and the advertisement putting effect and efficiency are improved.

The invention discloses a multi-platform-based advertisement creativity and putting strategy intelligent generation system, equipment and medium, multi-source data are integrated through a cross-platform creativity intelligent generation module to generate creativity materials adaptive to different platforms, and a cross-platform collaborative strategy optimization module is used for quantifying a platform collaborative effect and dynamically allocating budget. An advertisement creative life cycle is managed by means of a real-time dynamic optimization module, a strategy is adjusted based on multi-dimensional feedback, and finally, output of each module is fused through a unified decision engine to form a unified decision. The system realizes full-process intelligentization of advertisement originality and putting strategies, quantifies a cross-platform synergistic effect to improve budget distribution efficiency, constructs an advertisement originality life cycle dynamic management mechanism, realizes strategy real-time evolution through multi-dimensional feedback, and visualizes full-link data in combination with a unified decision engine, so that the advertisement putting return on investment is remarkably improved, and the advertisement putting efficiency is improved. The creative manufacturing cost is reduced, the strategy adjustment efficiency is improved, and an efficient and accurate multi-platform putting solution is provided for advertisers.

The invention relates to the technical field of advertisement putting, in particular to an AI-based accurate marketing advertisement putting method, which comprises the following steps: collecting multi-modal sensing data of a user terminal, extracting behavior information such as facial micro-expression, touch screen pressure and equipment rotation angular velocity, and generating an emotion feature vector through a pre-trained emotion classifier; matching the emotional characteristics with an emotional labeling matrix of the advertisement materials, and selecting the advertisement materials of which the emotional adaptation degree is higher than a threshold value to construct a candidate advertisement set; duration matching is carried out in combination with the user attention attenuation curve and the advertisement material display duration, and it is ensured that putting is completed before the attention critical point; when the user skips the advertisement, pupil focusing positions, finger tracks and acceleration information are extracted to generate a negative feedback feature matrix, the weight of the emotion classifier is dynamically updated, and a matching threshold value is optimized. According to the invention, an accurate delivery mechanism of emotion driving, attention perception and negative feedback self-learning is realized, and the personalized matching degree and delivery effect of advertisements are significantly improved.

The invention discloses a scenarized vehicle-mounted advertisement recommendation system for intelligent traffic, and relates to the technical field of intelligent traffic, and the system comprises a data collection module which collects a direction deviation angle, a speed matching degree and a path following rate in real time through a vehicle sensor and a navigation system, and obtains original behavior data; the feature extraction module is used for processing the original behavior data by adopting a time sequence analysis method, extracting dynamic features of steering frequency, pause time difference and signal acquisition frequency, and determining a behavior signal sequence; the correlation analysis module is used for calculating a correlation coefficient according to the matching degree of the behavior signal sequence and the navigation setting information on the aspects of time synchronism and environmental interference factors to obtain a correlation feature vector; according to the intelligent traffic-oriented scenarized vehicle-mounted advertisement recommendation system, through combination of multi-level behavior analysis and a recommendation algorithm, the pertinence of advertisement pushing and the user acceptability are remarkably improved, and an efficient advertisement putting effect is realized.

The invention provides a live broadcast advertisement effect real-time optimization method and system based on multi-modal AI, and relates to the technical field of advertisement optimization, and the method comprises the steps: collecting the multi-modal input information of a live broadcast scene, and extracting a feature vector after preprocessing; constructing a multi-modal-advertisement effect association graph, and integrating the multi-modal-advertisement effect association graph with the multi-modal knowledge graph to form a dynamically updated advertisement optimization knowledge network; based on real-time context awareness and a knowledge network, determining an optimal strategy through reinforcement learning evaluation sorting; the optimal strategy is converted into a specific instruction, and dynamic optimization is executed; collecting effect feedback data, and returning to a feature fusion and weight optimization link; the system comprises a multi-modal data acquisition and feature extraction module, a cross-modal feature alignment and knowledge network construction module, a reinforcement learning strategy generation and evaluation module and the like. According to the invention, the delay waiting time of the live broadcast advertisement optimization response is reduced, the real-time adaptability is improved, and the advertisement putting accuracy and conversion efficiency are improved.

The invention discloses a multi-modal dynamic feedback-based material generation method and device, computer equipment and a readable storage medium, and the method comprises the steps: firstly collecting user behavior, environment and emotion data, carrying out the preprocessing, mapping the data into a multi-modal feature vector, inputting a dynamic feedback learning model which is based on a Transform architecture and comprises a real-time feedback layer, and carrying out the real-time feedback of the multi-modal feature vector; and obtaining an initial advertisement content material. And then collecting real-time feedback data of a user aiming at the material, calculating multi-task loss and gradient through a real-time feedback layer, updating the model to output and optimize the advertisement content material according to the multi-task loss and gradient, and finally converting the optimized advertisement content material into a material conforming to a target platform format. According to the method, personalized, dynamic generation and cross-platform automatic adaptation of the advertisement content are realized, and the advertisement putting effect is improved.

The invention belongs to the technical field of advertisement putting, and discloses a multi-modal data driven intelligent advertisement putting system and method. Comprising the following steps: acquiring and analyzing historical behavior data of a user, extracting a periodic rule of user activity, and identifying candidate delivery windows; when it is detected that the current time is matched with the candidate delivery window, collecting real-time behavior data; performing feature extraction on the real-time behavior data to obtain situation features, and identifying key situation factors to form a user situation portrait; according to the situation portrait of the user, dynamically evaluating interruption cost and response probability, and judging whether the current time is a suitable delivery opportunity, and if so, performing advertisement delivery; according to the invention, comprehensive perception and analysis of user behaviors and situations are realized, and forced pushing under the situation that the user is not suitable for receiving the advertisement is avoided, so that the user experience is improved, and the accuracy and effect of advertisement putting are effectively improved.

The invention relates to the technical field of advertisement processing, in particular to an advertisement risk early warning method based on multi-modal feature fusion, which comprises the following steps: step 1, multi-modal data collection: collecting multi-modal data of an advertisement from multiple channels such as an advertisement putting platform and social media; 2, multi-modal data preprocessing, including text data preprocessing, image data preprocessing, video data preprocessing and audio data preprocessing; 3, performing multi-modal feature fusion, including early fusion, late fusion and hybrid fusion; 4, constructing a risk early warning model, wherein the risk early warning model is constructed in a mode of combining a deep learning model and a multi-layer perceptron; according to the method, the limitation of traditional single-mode detection is effectively broken through by integrating multi-mode data such as texts, images, videos and audios; the problems of poor detection effect, missing detection and the like of a traditional method in a complex advertisement form are effectively solved.

The embodiment of the invention provides a published content recommendation method and device, equipment, a medium and a product, and the scheme can comprise the following steps: filling a search keyword used for searching a target advertisement and candidate published content matched with the search keyword into a cue word template containing thinking chain guide information; and generating prompt information for being input into the large language model. Wherein the thinking chain guiding information is guiding information of a thinking chain of a recommendation reason generated for the candidate published content; the recommendation reason is a specific reason basis for putting the target advertisement by utilizing the candidate release content. And inputting the cue word information into a large language model to obtain recommendation information at least comprising the recommendation reason and a thinking chain corresponding to the recommendation reason. According to the scheme, the advertisement publisher can fully understand the recommendation basis of the recommendation system, the recognition degree and the satisfaction degree of the recommendation content are improved, and the overall user experience and the advertisement putting effect of the recommendation system can be improved.