32 results about "Online advertising" patented technology

The invention discloses an online advertisement intelligent pushing system based on big data, and relates to the technical field of advertisement pushing, the online advertisement intelligent pushing system comprises a data acquisition module, a data processing module, a data storage module, an intelligent pushing module and a user feedback module, the intelligent pushing module comprises a user interest analysis unit, a pushing opportunity unit and an effect evaluation unit, the user interest analysis unit analyzes the interest degrees of different advertisement categories, the pushing opportunity unit divides a day into a plurality of time periods, and determines the advertisement pushing strength and the advertisement pushing time periods, so that the preference of the user for the advertisement categories is analyzed through an interest evaluation algorithm, the most popular advertisement category is selected for pushing, and the advertisement pushing efficiency is improved. The optimal advertisement pushing time period and pushing strength are determined through the segmentation evaluation algorithm, the pushing effect is evaluated through the advertisement effect evaluation algorithm, the threshold value is set, the competitive advertisement influence is adjusted in real time, the advertisement strategy is dynamically adjusted in the high competitive time period, and the advertisement pushing accuracy is improved.

The invention relates to the technical field of online advertisement analysis based on artificial intelligence, and discloses an online advertisement analysis method and system based on artificial intelligence. By setting an advertisement set and a standard record thereof, each element of advertisement data acquisition is determined, and the problems of data source inconsistency and data confusion are solved. The advertisement set comprises text description, image data, video data and display timestamps, and it is ensured that data formats are unified. A text value is calculated through a character counting function, so that text information can be quantified; the unique color type number is obtained through pixel-by-pixel scanning of image values, and the problem of image data quantification is solved; the video playing duration is analyzed from the file, and time information standardization is ensured. Finally, the values of the text, the image and the video data are summed to obtain an advertisement comprehensive description value, unified processing of multi-modal data is realized, and data integration and cross-modal analysis effects are improved.

The present invention discloses a remarketing advertising effect prediction method and system based on multi-task learning, which belongs to the field of online advertising marketing information. The present invention learns and predicts multiple user behaviors (clicks / browsing / add-to-cart / purchase) after the advertisement is displayed, considers multiple influencing factors at the same time in the display link, automatically learns the correlation between features and targets, and solves the remarketing advertising effect prediction problem with one model. Specifically, knowledge is learned from massive data through offline feature design, target determination, sample construction and model training, and feature extraction and model scoring are completed for requests online in real time. The knowledge trained from the offline model is applied to the production environment to realize real-time remarketing advertising effect prediction. The present invention uses one model to realize the prediction of multiple indicators, which reduces the complexity of traditional multi-model solutions and overcomes the problem of poor training effect caused by sparse data of purchase / add-to-cart and other behaviors.

An online advertisement content platform providing system includes a server and an electronic device, wherein the server is configured to obtain at least one content of a specific product from the electronic device, wherein the at least one content includes an image of the specific product, acquire product attribute information of the specific product based on the at least one content, wherein the product attribute information corresponds to a plurality of product features, and provide, based on the product attribute information, recommendation information of a famous image identified based on a plurality of weight values related to the plurality of product features to the electronic device, wherein the recommendation information of the famous image includes at least one famous image.

This application relates to the field of online advertising API optimization, and discloses an online advertising API optimization method and device based on traffic statistics. The method includes: obtaining the traffic of the first online advertising API in the first time period and in the second time period respectively, and obtaining the traffic of the second online advertising API in the first time period and in the second time period; determining a first traffic difference between the traffic of the first online advertising API and the second online advertising API in the first time period, determining a second traffic difference between the traffic of the first online advertising API and the second online advertising API in the second time period, and determining the difference between the first traffic difference and the second traffic difference as the traffic difference fluctuation value; when both the first traffic difference and the second traffic difference are greater than or equal to a preset traffic difference, and the traffic difference fluctuation value is less than a preset traffic difference fluctuation value, optimizing the first online advertising API. This application can accurately locate the optimization direction of the online advertising API and improve the optimization effect of the online advertising API.

Currently, online advertising systems are primarily used to attract human attention and monetize it by selling products and services to humans. A superior way to monetize human attention specifically, and the attention of any intelligent entity more generally, is to focus it on solving valuable problems. This invention shows how to capture specific human (or non-human) expertise via online ads and then use that expertise to train advanced AI systems which then solve valuable problems using that expertise. Using the invention, Alphabet / Google, Meta, Amazon, Alibaba, ByteDance / TikTok, Microsoft, Apple, TenCent, Baidu, Twitter / X, Spotify, PubMatic, Pinterest, Snap and other online advertisers can significantly increase their online advertising revenue. The system and methods also enable existing online advertising technology to power AI, Artificial General Intelligence (AGI), and SuperIntelligent systems. The technology includes systems and methods that increase AI safety and maximize the chances of human survival and prosperity in the age of AI.

The present disclosure relates to a context online advertisement delivery method and device, a server and a storage medium, and relates to the technical field of computers. The method comprises: obtaining a text to be processed; in response to a user performing a context operation on the text to be processed, obtaining a target text unit and a target text, obtaining a word document corresponding table and a candidate sentence library; obtaining first target advertisement information and second target advertisement information according to the target text unit, the word document corresponding table, the target text and the candidate sentence library, providing the target text and / or the target text unit, the first target advertisement information and / or the second target advertisement information to the user. Thus, the user can establish a global view of the language structure and train the language sense to quickly master the language. At the same time, the user is provided with advertisement information related to the learning text, which improves the click rate of the advertisement while realizing the implantation of the advertisement scene.

A measurement platform configured to measure performance of an advertisement, the measurement platform comprising a demand-side platform (DSP), the measurement platform configured to send to an end user a survey configured to measure the advertisement's performance on behalf of a client DSP, wherein the measurement platform is further configured to update an exposed group of exposed users, wherein the measurement platform is further configured to update a control group of end users with similar statistical properties to the exposed group except that the control users have not seen the winning advertisement, wherein the measurement platform is further configured to send the survey to the end user, wherein the measurement platform is further configured to receive a survey response from the end user, wherein the measurement platform determines a metric measuring performance of the winning advertisement, wherein the measurement platform is further configured to send the metric to the client DSP.

The invention relates to a collaborative optimization method for feature construction and over-fitting tendency in a programmed advertisement, which comprises the following steps: acquiring original data of the programmed advertisement, and dividing the obtained original data of the programmed advertisement into a first-order feature combination, a second-order feature combination and a third-order feature combination according to feature attributes of the original data of the programmed advertisement; calculating an OPI value of each feature data in the first-order feature combination, the second-order feature combination and the third-order feature combination; performing risk assessment by using the OPI value of each feature data; performing risk processing on the high-risk and medium-risk feature data; using the processed feature data as a training set to train a CTR prediction model to obtain a trained CTR prediction model; using the trained CTR prediction model to predict the CTR value of the daily online advertising; the problems of high over-fitting risk, large resource waste, poor alleviation effect and high labor cost in the prior art are solved, and the method has significant practical value in real-time bidding and accurate delivery scenes of programmed advertisements.

The application discloses a kind of multi-channel advertisement intelligent distribution control system and method based on deep learning, comprising the following steps: collecting multi-source advertisement delivery data, structured input dataset including user behavior sequence, advertisement content sequence and channel attribute sequence are constructed;Structured input dataset is encoded to feature vector, and three kinds of feature vectors are generated;Three kinds of feature vectors are input into ternary gate fusion structure, and fusion feature representation is generated;Fusion feature representation is input into improved PLE model, and click rate prediction value, conversion rate prediction value and budget cost prediction value are output;Strategy scoring function is constructed based on the above three prediction results, and the distribution score of candidate advertisement and channel combination is generated;According to distribution score ranking, the highest score combination is obtained, distribution instruction is generated and output to multi-channel advertisement system.The application improves the precision and cost control ability of advertisement distribution strategy under multi-target delivery, and is suitable for large-scale online advertisement delivery scene.

Online advertisers may demand compliance with certain standards for the content of emails and other digital content with which its advertisements may be associated. Emails may contain control objects directing users to a web page showing content related to oil change coupons. However, in some systems, methods, and processes, a small variable proportion of users may instead be directed to a different sequence of pages to conduct a compliance check on the contents of the email received by the user. The compliance check sequence of pages may offer the user an incentive to forward the email to a specified email address for subsequent compliance review. Delivery of an incentive may be conditioned on successful receipt of the forwarded email.

This application discloses a user profiling analysis method and server based on online advertising. The method includes: first, acquiring the target user's long-tail search keyword sequence and community interaction record sequence; merging the two sequences into a user behavior action unit chain and generating a set of behavior evolution relationship edges to form a user behavior semantic script; then, performing dependency parsing to generate an intent discourse structure containing a set of subordinate relationship identifiers and a set of causal modification relationship identifiers; performing hierarchical attention propagation operations to generate a hierarchical attention distribution map; and finally, extracting key plot nodes and semantic transition relationship nodes, mapping them to a set of advertising placement semantic anchor points, and generating a user profile feature cluster. By constructing a user behavior semantic script and parsing its intent discourse structure, this application can generate an accurate profile feature cluster that reflects the hierarchical organization and evolution of user interests.

The invention relates to a system for sharing and monetizing data through web3 technology enabled router, wherein the system comprises a plurality of router devices for connecting one or more user devices, and functions as a gateway for communications between the user device and one or more servers. Further, the router device comprises a remote management server module for managing the one or more servers, a data acquisition module for gathering all the data from the user device, an advertisement blocking module for blocking online advertisement in a web browser on the user device, a voice response module for responding to the user' command and a federated learning module for training, constructing and updating a model in a central learning system.

Advertising methods, advertising systems, and advertising management methods. Technology is provided to enable online advertising operators to more easily manage outdoor advertising. The advertising method includes the following steps: a second management device obtains first advertising information representing an advertisement for a first store from a first management device, the first management device managing advertising information for multiple stores that actually exist, including the first store; a display device obtains the first advertising information from the second management device; the display device displays the advertisement for the first store in a public place at a size viewable by multiple people based on the first advertising information; the second management device obtains visitor access information for the first store; and the second management device sends information related to advertising costs to the first management device based on the access information, the visitor being a person who visited the first store based on the advertisement displayed in the public place.

Identifying potentially unfair practices in content and serving relevant advertisements is described. A total risk score associated with an online advertisement is determined by a computer system based on a first risk score and an issue risk score. The first risk score is based on online content associated with the online advertisement and the issue risk score is based on one or more risk attributes associated with the online advertisement. The total risk score is transmitted to a client computer, where the total risk score is displayed through a user interface in conjunction with the display of the online advertisement on the client computer.

The present disclosure provides a system for detection of online advertisement fraud and commerce fraud. The system includes a first step of collecting a first set of data from a plurality of components associated with each device of a plurality of devices and receiving a second set of data associated with each device of a plurality of third party devices. The system includes yet another step of calculating a probabilistic score for detection of the online advertisement and the commerce fraud in real-time. The system includes yet another step of analyzing the first set of data and the second set of data after a periodic interval of time. The system includes another step of detecting the online advertisement fraud and commerce fraud based on the analysis of the first set of data and the second set of data.

A system and method is provided for dynamically managing advertiser affiliate links for online marketing. A global redirect server accepts a visitor's web browser request for a product or non-product webpage associated with an affiliate network advertiser URL, and returns a corresponding product or non-product webpage to the visitor that is corrected for the visitor's geographic region if such a webpage exists, or returns the originally requested webpage if it does not. The server determines the visitor's geographic region based upon request metadata, and can generate a corrected webpage that affords analytic and commission tracking even if no corresponding affiliate network exists in the determined geographic region. The server includes a database for locating products within the determined geographic region that are comparable to the product of the product webpage request, in the event that the exact product is not available in the geographic region.

In a computer-implemented method, a selected online advertisement is automatically directed to, or online activity is automatically tracked and recorded from, an online user interface device which corresponds, at the time the advertisement is directed or the activity is tracked, to a first online access identifier. The advertisement is directed or the activity is tracked based on information from a user profile that references or includes a first set-top box identifier. That information is derived at least in part from automatically collected data related to user behavior with respect to at least one television advertisement delivered to a set-top box, which set-top box corresponds, at the time of that user behavior, to the first set-top box identifier. The first online access identifier is associated with the first set-top box identifier, by being referenced or included in the user profile, by being associated in a database, or otherwise.

The invention relates to the technical field of data updating, in particular to a computer network data instant updating method based on cloud computing, which comprises the following steps: after collecting network data information from a data plane in real time, decomposing the network data information into micro-batch updating operations according to time intervals, and then converting the micro-batch updating operations into an updating dependency graph according to the network data information; the method comprises the following steps: updating a dependency graph, constructing an updating message according to the updating dependency graph, finally enabling a controller to execute a distributed updating operation by utilizing the updating message, tracking an updated operation process and a data flow by using a monitoring tool in the distributed updating operation process, and adjusting an updating strategy according to a tracking result. The problems of time delay and data processing amount limitation existing in social media content updating, online advertisement putting, logistics process updating and the like are solved.

The invention discloses a multi-channel advertisement intelligent distribution control system and method based on deep learning, and the method comprises the following steps: collecting multi-source advertisement putting data, and constructing a structured input data set comprising a user behavior sequence, an advertisement content sequence and a channel attribute sequence; performing feature vectorization coding on the structured input data set to generate three types of feature vectors; inputting the three types of feature vectors into a ternary gating fusion structure to generate fusion feature representation; inputting the fusion feature representation into an improved PLE model, and outputting a click rate prediction value, a conversion rate prediction value and a budget cost prediction value; constructing a strategy scoring function based on the three prediction results, and generating distribution scores of candidate advertisement and channel combinations; and selecting a combination with the highest score according to the distribution score sequence, generating a distribution instruction and outputting the distribution instruction to the multi-channel advertisement system. The method improves the precision and cost control capability of an advertisement distribution strategy under multi-target putting, and is suitable for a large-scale online advertisement putting scene.

The invention discloses a full-link generation type full-modal click rate prediction method and system, and relates to the field of online advertisement and recommendation systems.The method comprises the steps that multi-modal data and user features of texts, images and videos are preprocessed, and serialization, standardization and vectorization processing is carried out; aligning the multi-modal representation to a unified discrete potential space by using a multi-scale residual quantization encoder; generating personalized dynamic parameters of the target prediction network from the user state and scene context hierarchy based on the super network; generating a next item in which the user is interested and a click probability by adopting an autoregression learning normal form in combination with the personalized dynamic parameters; and performing end-to-end training by jointly optimizing the autoregression loss and the CTR prediction loss. According to the method, the nonlinear relationship and high-order feature interaction between the user and the article are effectively captured, and the accuracy of click rate prediction and the recommendation effect are remarkably improved by utilizing multi-modal information and a personalized parameter generation mechanism.

A system including one or more processors, and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: segmenting each respective user of one or more users into a respective user state of one or more respective user states; predicting, using a machine learning LTV update model, a respective LTV for each respective user of the one or more users; determining a respective transition path from the respective user state of the respective user to a different respective user state; determining a respective online advertisement to transition a respective user of the one or more users from a respective transition state to the different respective user state; and displaying the respective online advertisement for the respective user of the one or more users. Other embodiments are disclosed herein.

This technology provides online advertising businesses with the ability to more easily manage outdoor advertising. [Solution] The advertising method includes: a second management device acquiring first advertising information indicating an advertisement for the first store from a first management device that manages advertising information for multiple existing stores, including the first store; a display device acquiring the first advertising information from the second management device; the display device displaying the advertisement for the first store in a public place in a size that can be viewed by multiple people, based on the first advertising information; the second management device acquiring visit information of visitors to the first store; and the second management device transmitting information regarding advertising costs to the first management device based on the visit information, wherein the visitor is a person who visited the first store based on the advertisement for the first store displayed in the public place.

According to various embodiments, the online advertisement content platform providing system includes a server and an electronic device, wherein the server is configured to obtain at least one content of a specific product from the electronic device, wherein the at least one content includes an image of the specific product, acquire product attribute information of the specific product based on the at least one content, wherein the product attribute information corresponds to a plurality of product features, and provide, based on the product attribute information, recommendation information of a famous image identified based on a plurality of weight values related to the plurality of product features to the electronic device, wherein the recommendation information of the famous image includes at least one famous image, wherein the electronic device is configured to display a first screen including the at least one famous image and a predetermined number of famous features among the plurality of famous features based on the provided recommendation information of the famous image, wherein based on the specific product being the first product, the predetermined number of famous features includes a first group of famous features among the plurality of famous features, and based on the specific product being the second product, the predetermined number of famous features includes a second group of famous features among the plurality of famous features, and the second group of famous features may be at least partially different from the first group of famous features.

A performance internet marketing self-optimizing, anthropomorphic, artificial intelligence-based system and method to quote, compare and purchase products from a provider, including personal lines insurance, commercial lines insurance, property insurance and casualty insurance. The system and method collects information from an online advertising platform during the process and returns the collected information to the online advertising platform for improvement of the online advertising platform.

Example methods, apparatuses, and systems are presented that allows a user to make a secure purchase online, directly through accessing an online advertisement and without being redirected to multiple, cumbersome webpages to process different pieces of information to complete the transaction, while still leveraging existing e-commerce entities, such as existing payment platforms and existing ad / content networks. The present system includes a commerce ads engine (CA engine) that interfaces with the user through an app associated with the CA engine, a tokenization platform for authentication of the user, and a merchant providing relevant offer and check out information about a product being advertised in an online ad. The present system integrates new information embedded into online advertisements that interfaces with the CA engine and allows for the user to reliably provide authentication while purchasing the desired product, and without accessing or being redirected to multiple websites as is conventionally done.

This application relates to a data-driven, efficient, and precise online advertising delivery method and system, belonging to the field of advertising delivery technology. It addresses the problem that different audience groups have varying levels of acceptance of ad pushes, and pushing ads at uniform intervals can easily lead some audiences to feel that the ads are too frequent, affecting their effectiveness. The method includes: calculating the total time a user has owned a relevant product based on the time of their most recent purchase and the current time; calculating the time difference between the total time a user has owned the relevant product and the average replacement time of the corresponding product; analyzing and determining the advertising delivery strategy based on the comparison between the time difference and a preset time difference and the corresponding advertising delivery strategy; and executing the advertising delivery strategy. This application has the following effects: making the ad push interval more suitable for each individual's situation, thereby making the advertising effect more efficient and precise.

The systems and techniques described herein relate to predicting user conversions in online advertising. Input data associated with user and advertisement features may be processed through neural networks to generate embedding representations or feature cross representations. A multi-task layer calculates probabilities associated with multiple user actions like clicks, page views, sign-ups, or purchases. Click-through and view-through conversion probabilities may be calculated to generate a score. The systems and techniques described herein perform predictions on multiple types of user actions despite data sparsity and negative transfer challenges, enhancing advertisement targeting and improving conversion metrics.

The invention discloses an online advertisement putting optimization method based on a knowledge graph, and the method comprises the following steps: obtaining online advertisement putting data, generating a corresponding graph structure, and building a putting optimization parameter set; retrieving candidate delivery configuration in the graph structure, and obtaining a delivery sub-graph; generating a candidate arm set and determining rule constraint information; executing feasible region reconstruction processing to obtain a feasible candidate arm set; calculating a structural uncertainty index for each candidate arm, and determining an arm-level implicit exploration parameter of the candidate arm; generating selection probability distribution of a feasible candidate arm set according to an EXP3-IX decision rule, and collecting delivery feedback data of a current delivery action; and retrieving an interpretation path set corresponding to the release feedback data, updating the accumulated loss value of the target candidate arm, and regenerating the selection probability distribution. According to the method, knowledge graph constraint reconstruction and gated EXP3-IX online updating are combined, and robust continuous putting optimization on the premise of compliance is achieved.

The present invention provides improved techniques for identifying online advertisement laundering and online advertisement injection that may occur during the serving of advertisements to browsers. For example, in one embodiment, a method of identifying fraudulent advertising activities may comprise obtaining information identifying a computer system of every party involved in supplying an advertisement to an end user device, determining whether a computer system of a first party and a computer system. of a second party provide websites and are not intermediary computer systems, determining whether the computer system of the first party provides a website of a type undesirable to advertisers, determining whether the computer system of the second party has usage and traffic characteristics indicative of not being an intermediary computer system, and determining a frequency of recurrence of the determined. conditions.