431 results about "Click-through rate" patented technology

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 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.

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 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.

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 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 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.

The invention provides a power grid standard intelligent recommendation method based on reinforcement learning and post system management. The power grid standard intelligent recommendation method comprises the following steps of 1, collecting and cleaning power grid standard data; step 2, post-standard system knowledge graph construction; 3, designing a reward function for the power grid standard recommendation system, and realizing a personalized recommendation strategy by taking post responsibilities and skill levels as dynamic weight parameters; and step 4, post-driven online incremental training is carried out. According to the method, a reinforcement learning dynamic optimization engine is adopted to learn user behaviors (click rate and task completion score) in real time, and a standard recommendation strategy is adjusted; and when the standard is updated, the post capability weight is automatically adjusted, closed-loop feedback optimization is realized, and the problem of updating delay of a traditional system is solved.

The invention relates to an intelligent advertisement putting method and system based on big data analysis, and the method comprises the following steps: S1, obtaining multi-dimensional data related to advertisement putting, and carrying out the preprocessing of the data; s2, constructing a user portrait based on the preprocessed multi-dimensional data; s3, constructing a CTR prediction model, and predicting the advertisement click rate by using GBDT and LR based on the user portrait, the advertisement features and the context features; s4, based on the user portrait and the predicted advertisement click rate, performing user matching based on an artificial intelligence model; and S5, based on a matching result in the S4, generating optimal advertisement content for the user by using a recommendation algorithm, and automatically optimizing an advertisement title and a copywriting by using text generation. According to the method, the advertisement click rate, the conversion rate and the advertisement revenue are improved, and more efficient and accurate content consumption experience is created for the user.

The invention provides a click rate model training method, and an article recommendation method and device.The click rate model training method comprises the steps that a vector fusion layer in an initial click rate model is adopted to fuse a first user feature, a first cooperation feature and a first article feature sequence of a sample object to obtain a first fusion vector; performing first feature processing on the first fusion vector by adopting a feature processing layer in an initial click rate model to obtain a second fusion vector; and according to the first fusion vector, the second fusion vector and the first material feature sequence of the first material for displaying the plurality of sample articles, determining weights of the plurality of sample articles, so as to train the initial click rate model according to the weights of the plurality of sample articles, thereby improving the effectiveness of training the initial click rate model, and improving the accuracy of training the initial click rate model. Therefore, the click rate of each article predicted based on the trained click rate model can accurately recommend the article to the user, and the user experience is improved.

The invention relates to the technical field of man-machine interaction and digital display processing, and discloses an exhibition scene dynamic interactive advertisement generation method, which comprises the following steps: S1, receiving advertisement content data uploaded by exhibitors, and constructing the content data into displayable advertisement data units, wherein each advertisement data unit comprises a display position attribute and a display weight attribute; s2, collecting behavior data of the user in the display space, wherein the behavior data comprises a touch position, a sight focus point or a gesture interaction point; and S3, on the basis of the spatial association relationship between the display position of the advertisement data unit and the user behavior data, constructing a behavior-driven potential energy field for representing user interest distribution. According to the method, the advertisement content layout adjustment is guided by constructing the potential energy field model driven by the user behaviors, and the dynamic association matching of the users is realized, so that the on-site users can always contact the personalized and high-relevancy advertisement content in the moving process, and the advertisement click rate and the conversion efficiency are remarkably improved.

The invention discloses an e-commerce advertisement accurate recommendation system based on deep learning, and relates to the technical field of e-commerce, and the system comprises the following modules: an entity index module used for constructing a node registry; the hypergraph construction module is used for continuously monitoring an event stream and constructing a dynamic hypergraph; the initial embedding module is used for extracting a node embedding tensor and a hyperedge embedding tensor; the graph representation learning module is used for extracting a final node embedding representation tensor and a final hyperedge embedding representation tensor by improving a GraphTransform model; the click rate estimation module is used for outputting the predicted click rate of each candidate advertisement; and the sorting decision module is used for screening a candidate advertisement list. According to the method, the limitation that a traditional recommendation method depends on simple characteristics and neglects high-order association and dynamic evolution information is overcome, and a powerful solution is provided for realizing efficient, accurate and personalized e-commerce advertisement recommendation.

The invention discloses a cross-border e-commerce marketing strategy intelligent management system based on big data, and relates to the technical field of cross-border e-commerce marketing strategy management.The cross-border e-commerce marketing strategy intelligent management system utilizes a heterogeneous graph neural network to construct a user-commodity-culture relation graph, and dynamic weights are given to nodes through an attention mechanism; differences of consumers in each region in historical transactions, click rates and culture similarities are effectively captured, so that the problem of insufficient adaptability of a traditional fixed rule model in a multi-culture market is solved; carrying out emotion and semantic feature extraction on a multi-language comment text by adopting a Transform structure, and realizing rapid self-adaption of the model under the condition of data scarcity in combination with a meta-learning strategy; according to the combination, deep semantic information reflecting emotions and preferences of different regions can be accurately extracted, and cross-culture adaptability recommendation results are generated by fusing image embedding and text processing results, so that marketing contents better meet personalized requirements of consumers in various regions.

The invention discloses a distributed personalized recommendation method and system, and relates to the technical field of machine learning, the method comprises the following steps: a client trains a local recommendation model based on a local data set, and optimizes the model by using a slimming module and a feature sharpening module; the client encrypts and uploads the trained model parameters to the server; the server aggregates the model parameters uploaded by different clients, generates global model parameters and issues the global model parameters to each client; and the local recommendation model of the client configures global model parameters, and performs click rate prediction in combination with local data to generate a personalized recommendation result for the user. According to the method, a model slimming and feature sharpening mechanism is adopted to optimize a recommendation model structure, model parameters can be reduced, feature representation can be enhanced, and improvement of the efficiency and accuracy of the recommendation model is facilitated. In combination with the differential privacy technology, noise disturbance is introduced in the data transmission and calculation process, so that the calculation and communication cost is reduced, and meanwhile, the user privacy protection capability is enhanced.

The invention discloses a global intelligent marketing method based on multi-modal information fusion. The method comprises the following steps: firstly, collecting multi-source heterogeneous data, extracting text, vision and behavior characteristics by using deep learning, and mapping the text, vision and behavior characteristics to a public semantic space for fusion through an attention mechanism; and then, constructing a dynamic user portrait based on the fusion features and identifying a user intention stage. On the basis, the click rate is estimated by using a double-tower network, and a dynamic bidding strategy is generated in combination with a reinforcement learning DQN model and PID control. And finally, aiming at customer inquiry, generating a quoted price in combination with a cost model or performing automatic negotiation by using a time-dependent game strategy, and updating model parameters in real time through online incremental learning. Through full-link intelligent closed-loop control, accurate user intention recognition, efficient advertisement putting and automatic business negotiation are realized, the marketing conversion rate is effectively improved, and the operation cost is reduced.

The invention relates to an advertisement click-through rate prediction method and prediction system based on multi-scale feature fusion. The method comprises the following steps: analyzing a feature tag of an advertisement object; matching a reference advertisement object with the advertisement object by using the feature tag; calculating a predicted click rate of the advertisement object and comparing the predicted click rate with an actual click rate of the reference advertisement object to obtain a comparison result; calculating the error degree of the comparison result, replacing the reference advertisement object according to the error degree, and repeatedly calculating the comparison result; drawing a comparison result curve by using the comparison result and the comparison result ranking; and using the last comparison result in the sequential sequence as the predicted click rate of the advertisement object. According to the advertisement click-through rate prediction method and prediction system based on multi-scale feature fusion disclosed by the invention, the advertisement click-through rate is calculated by using a reference mode, and the reference content is corrected according to the calculation result, so that a more accurate reference sample can be obtained, and more accurate click-through rate data can be obtained based on the reference sample.

The invention provides a title generation model training method and device, a title generation method and device, electronic equipment, a medium and a program product, and relates to the technical field of artificial intelligence, in particular to the fields of model training, reinforcement learning, large language models, generative artificial intelligence and the like. According to the implementation scheme, a title generation model is trained; training a first reward model associated with the click rate for making a first score for each candidate title in the plurality of candidate titles; training a second reward model associated with the content quality of the title for making a second score for each candidate title; fusing the first score and the second score of each candidate title in the plurality of candidate titles to obtain a reward score; and based on the reward score corresponding to each candidate title, training a title generation model to increase the generation probability of the candidate title with the higher score in the plurality of candidate titles and reduce the generation probability of the candidate title with the lower score at the same time.

The invention discloses an e-commerce design automatic workflow optimization system, which is applied to commodity display image and advertisement map design of an e-commerce platform, combines an artificial intelligence technology and an automatic workflow, and optimizes an e-commerce design flow. Comprising an intelligent template recommendation module, an adaptive design optimization module, an intelligent typesetting and custom adjustment module, a cross-platform compatibility and adaptation module, a data driving optimization module and an A / B test and intelligent evolution module. Through intelligent template matching, automatic typesetting and AI optimization, the e-commerce advertisement design efficiency is improved, the visual quality is optimized, and the user click rate and the conversion rate are improved. And by adopting intelligent size adaptation, cross-platform compatibility is realized, and the adjustment cost is reduced. An A / B test and machine learning are combined, the advertisement is optimized, and the ROI is improved. Based on user portrait analysis and personalized recommendation, the advertisement style is adjusted, and the personalized experience is enhanced. Through intelligent advertisement generation and automatic optimization, the dependence on professional design skills is reduced, and the cost is reduced.

The invention provides a big data advertisement putting method and system based on a cloud platform. The method comprises the following steps of performing multi-source data collection and preprocessing; dynamically constructing a user portrait; performing multi-dimensional analysis on the behavior pattern; target audience subdivision is based on a Gaussian mixture model; dynamically generating personalized advertisement creativity; a step of optimizing a multi-target delivery strategy; a step of real-time bidding and bidding optimization; a step of putting execution and real-time monitoring; a step of effect evaluation and multi-dimensional feedback; and carrying out adaptive system adjustment and iterative optimization. The big data advertisement putting method based on the cloud platform has the advantages that putting precision and efficiency are improved, audiences are subdivided through complex statistics, dynamic creativity is generated, advertisement correlation is enhanced, the predicted click rate is increased by 15-20%, and budget waste is reduced. And the cloud platform supports stream processing and automatic adjustment, so that the system can quickly respond to market changes, and the delay is reduced to a millisecond level.

Aspects of the disclosure are directed to text to image generative models fine-tuned to generate images that account for performance in addition to quality. For example, in a digital content domain, the generated images can be not only visually appealing but perform well as advertising assets, e.g., result in improved click through rate and / or conversion rate. Accounting for performance and quality can reduce processing cost and memory usage when generating images from text prompts, as the resolution of the image can be balanced with its function, allowing for reduced quality images that can still perform well.

The embodiment of the invention relates to an information display method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring current behavior data of a target video in a playing process; the target video belongs to an object recommendation type video; based on the current behavior data, identifying a switching intention of the target video, and predicting a target display moment of an object acquisition control corresponding to the target video; and displaying the object acquisition control in a preset area of the target video based on the target display moment. Thus, before the user slides to switch the target video, the object acquisition control can be displayed in advance at a faster response speed, and the display probability of the object acquisition control is improved, so that the click rate of the object acquisition control and the user conversion rate of the target video are improved to a certain extent.

The invention relates to an intelligent advertisement putting optimization method based on a novel consumption scene, and the method comprises the following steps: S1, fusing the multi-dimensional behavior data of a user, and carrying out the precise user portrait modeling; s2, dynamically evaluating the mood state and preference change of the user based on semantic and emotion analysis of the staying chapters of the user; s3, automatically labeling an advertisement insertion point through an NLP technology, and dynamically generating a putting strategy; s4: according to the user portrait, the user mood state and the preference change, automatically designing the advertisement content and the emotion adaptive copywriting by using the generative AI; and S5, constructing a stream processing architecture, monitoring and dynamically analyzing the advertisement effect in real time, and automatically triggering pause and adjustment of an advertisement strategy if the user behavior index is abnormal. According to the invention, the click rate and conversion rate of the user can be effectively improved, and the dislike emotion of the user to the advertisement is reduced.

The invention provides a recommendation information display method and device, electronic equipment, a medium and a program product, and relates to the technical field of computers.The method comprises the steps that a client displays a search result for a search keyword on a first page, and when target input used for quitting display of the search result is received, the search result is displayed on the first page; sending a search request to a server; the server side responds to the search request of the client side, generates target recommendation information according to a user portrait of a target user, and sends the target recommendation information to the client side; the client displays a second page, wherein the second page comprises the target recommendation information; wherein the second page is a page triggered and displayed by the client based on the target input of the target user on the first page or the search input of the target user on the home page of the client. The method can be used for improving the click rate and the conversion rate of the content application platform.

The invention discloses an intelligent short message anti-interception method and device based on AI deep learning, and a storage medium, and the method comprises the steps: setting a short message sending demand obtaining module which is used for obtaining the short message content which needs to be sent by each client; an information classification module is arranged to detect and automatically classify short message content information needing to be sent; an intelligent short message reconstruction module is arranged, and an intelligent short message and a short message sending scheme are regenerated by using short message content information provided by a client; a short message interception simulation module is arranged and used for simulating short message interception and evaluating a short message sending success rate and a click rate; when the sending success rate is lower than a threshold value, the short message interception simulation module feeds back a reason possibly causing interception to the intelligent short message generation module, and the intelligent short message generation module improves short message content in a targeted manner and regenerates an intelligent short message and a short message sending scheme until the sending success rate is evaluated to be higher than the threshold value; a user confirmation module is arranged, and when the sending success rate is higher than a threshold value, the intelligent short message and a sending scheme thereof can be fed back to a client.