94results about How to "Improve click-through rate" patented technology

The invention provides an advertisement recommendation method and system based on a fusion neural network. The method comprises the steps of receiving real-time behavior data of a user; based on the real-time behavior data, a user interest map is dynamically constructed, and the user interest map comprises interested nodes of the user and the incidence relation between the interested nodes; analyzing the user interest map in real time by using a fusion neural network model; screening out a high-correlation advertisement set as candidate advertisements according to the preference probability; and in combination with the current scene in which the user is located, selecting a target advertisement from the candidate advertisements for pushing so as to improve the click rate and the conversion rate of the target advertisement. According to the technical scheme provided by the invention, the advertisement recommendation effect can be effectively improved.

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

The invention relates to the technical field of advertisement data processing, and discloses a method and device for achieving creativity generation by means of AI, and the method comprises the steps: obtaining user generation content from each social media platform in real time, and collecting hot topics of a current market; performing semantic analysis and sentiment analysis based on the hot topics to generate hot portraits; performing matching degree calculation on the hot portraits and target audience portraits of the product, and screening the hot portraits with the highest matching degree as the hot portraits of the target audience portraits; according to the hotspot portraits of the target audience portraits, performing two-dimensional screening in combination with the popularity of the hotspots to obtain target hotspot portraits; and inputting the target hotspot portrait and the associated product information into a preset deep learning model based on semantic matching, and outputting originality. The method has the advantages that the timeliness of the creativity is guaranteed, and the click rate and the conversion rate of the creativity are improved.

The invention relates to the field of advertisement putting, and particularly discloses an internet advertisement accurate putting system and method based on big data, and the method comprises the steps: firstly obtaining user basic data, user reading behavior data and user reading novel preference data, and then carrying out the feature extraction and correlation analysis of the three data through a deep learning technology, and finally, a personalized advertisement putting strategy is generated through a generator, so that accurate internet advertisement putting is realized, advertisement content is ensured to be related to interests and demands of users, and the click rate and conversion rate of advertisements are improved.

The embodiment of the invention provides a data processing method and device, equipment and a medium, and the method comprises the steps: responding to a search operation for query information, and displaying a first result page corresponding to the query information; in the first result page, displaying a search result corresponding to the query information and a search extension floating layer associated with the query information; the search extension floating layer is independently displayed on a first result page, and a tag page column of the first result page comprises a search extension tag; and in response to a detail viewing operation for the search extension floating layer, displaying a concerned topic directory associated with the query information in a channel page corresponding to the search extension tag. By implementing the embodiment of the invention, the search traffic can be improved.

The invention belongs to the technical field of artificial intelligence, and discloses an advertisement planning method and system based on artificial intelligence. Comprises: obtaining advertisement feature data; performing primary processing on the obtained advertisement feature data to obtain a primary advertisement feature data set; dividing audience groups by using a K-means algorithm based on the preliminary advertisement feature data set; extracting a keyword sequence based on audiences; based on the keyword sequence, utilizing a generative adversarial network model to generate an advertisement copywriting sequence; based on the advertisement copywriting sequence, similarity and fluency are calculated, a comprehensive score is obtained through weighted average, and the advertisement copywriting with the highest comprehensive score is selected as final output; according to the method, the correlation and attraction of the advertisement copywriting can be effectively improved, the advertisement putting effect is optimized, the click rate and the conversion rate of the user are finally improved, and higher investment return is brought to an advertiser. And the creativity and the delivery effect of the advertisement are greatly improved.

The invention discloses an advertisement pushing method and device based on artificial intelligence and a medium, and relates to the technical field of advertisement pushing, and the method comprises the steps: collecting data information of a user, carrying out the preprocessing operation, and dividing the data into a scene data set and a user interest data set; constructing a scene recognition model, and training the scene recognition model through the scene data set; constructing a user interest model, and training the user interest model through the user interest data set; analyzing the advertisement content, extracting an advertisement keyword, defining a scene keyword for the scene according to the current scene of the user, and matching and pushing the scene keyword; and carrying out effect evaluation and optimization on the pushed advertisement. According to the method, advertisements related to the current scene can be pushed to the user through scene perception advertisement pushing, so that the click rate and the conversion rate of the advertisements are improved, the satisfaction degree of the user is improved, and the loyalty of the user to the brand is enhanced.

The invention discloses a commodity recommendation system based on artificial intelligence, and the system comprises a user behavior tracking module which is used for collecting and processing the multi-source behavior data of a user; the commodity feature extraction and fusion module is used for acquiring and analyzing multi-modal information of commodities; the dynamic updating module is used for adjusting a recommendation list in real time according to instant user behaviors and long-term preferences in combination with short-term memory and long-term memory; the situation awareness module takes external factors such as time, place and weather as input; the explanatory enhancement module is used for adding an explanatory layer for a black box model by using an explanatory machine learning model; the personalized sorting optimization module is used for dynamically adjusting the feature weight and improving the click rate and the conversion rate; and the privacy protection module is used for protecting user privacy by adopting a differential privacy technology. According to the commodity recommendation system and method provided by the invention, many problems existing in a traditional recommendation system are overcome, and a more efficient, more intelligent and safer commodity recommendation service is realized.

The invention relates to the field of artificial intelligence shopping, in particular to a shopping mall system of an intelligent search interest recommendation algorithm, which comprises a registration and login module used for selecting a login identity and a login mode and inputting login information; the factory cloud supply module is used for updating and deploying the commodity supply information in real time; the merchant service module is used for managing commodity orders, counting service of buyers and analyzing sold commodities; the promotion and distribution module is used for carrying out advertisement putting and promotion activity promotion based on a digital transmission channel, and is also used for helping merchants to carry out management setting on distributors and formulate distribution levels, regional distribution and shareholder bonuses; the financial module is used for providing balance and points of shop members for merchant users and providing commission calculation and commission withdrawal; according to the invention, the enterprise operation efficiency is improved, the cost is reduced, the brand influence and market competitiveness are improved, and an efficient, intelligent and integrated business management system is realized.

The invention relates to the technical field of advertisement interception and optimization, and discloses a real-time advertisement interception and optimization method, which comprises the following steps of: when an advertisement appears on user equipment, monitoring closing or browsing operation of a user, obtaining an advertisement type preferred by the user, and forming an advertisement type accepting set of the user; meanwhile, software currently opened by a user is obtained, and user states including working and entertainment states are judged; executing a first interception optimization strategy in the working state; obtaining the current recreational activities of the user in the recreational state, and executing different interception optimization strategies according to the recreational activities performed by the user; the user state is obtained according to the software currently used by the user, and all or part of advertisements are selected to be intercepted according to the state, so that the relationship between the advertisement display frequency and the user experience is effectively balanced, and the interference of the advertisements on the normal use of the user is avoided.

The invention provides a commodity recommendation method and device, and the method comprises the steps: obtaining a recalled commodity set for each user through employing a multi-path recall mode, and enabling the multi-path recall mode to comprise at least two of label recommendation recall, collaborative filtering recommendation recall, hot recommendation recall, user historical recall and vector recall; a user browsing feature, a user click feature, a user purchase feature, a user customer level feature, a user gender feature and a commodity differentiation feature are constructed, a LightGBM algorithm model is adopted to perform fine ranking on the click rate to obtain a fine ranking sequence for each user, and the fine ranking sequence for each user is scattered according to a second-level category. And forming a mixed arrangement sequence for each user. According to the scheme, the number and types of commodities recommended to the user by the online shopping website are enriched, and the click rate and conversion rate of the recommended commodities are improved.

The invention relates to the technical field of media traffic distribution, and provides a resource niche traffic distribution method, device and equipment, a readable storage medium and a computer program product. The method comprises the steps of obtaining a traffic estimation value of a target time period according to historical traffic decomposition data and a traffic estimation model; according to a target user preference factor corresponding to the target time period and a target traffic distribution proportion between the operation plans, under the condition that a user click rate threshold and a traffic contract display amount are satisfied, distributing the traffic estimated value of the target time period, and obtaining a first traffic distribution target value of each type of content in the target time period; wherein different operation plans correspond to different types of contents; and for any type of content, determining a plurality of resource nichs of any type of content, and obtaining a second traffic distribution target value of the plurality of resource nichs in the target time period according to the first traffic distribution value of any type of content in the target time period and the traffic matching model. By adopting the method, the flow utilization rate and the user click rate can be improved.

The invention relates to an intelligent customer service method, and aims to provide personalized and accurate customer service reply through advanced technical means. The method comprises the following steps: firstly, acquiring and analyzing a query request of a user regardless of the form of text, voice or image; thirdly, intention recognition is conducted through a preset response model, a user portrait is generated in combination with historical interaction data of the user, and customer service requirements are predicted according to the user portrait. And by correcting the prediction intention data and considering the user preference, an accurate customer service reply is generated. In addition, the system also collects subsequent behavior data of the user, constructs a service scoring data set, and continuously optimizes the service model by using a machine self-learning algorithm. According to the method, the user satisfaction can be effectively improved, the click rate and the conversion rate are increased, the response time is shortened, the problem solving rate is improved, and continuous service optimization and performance improvement are realized.

The invention relates to an Internet advertisement system management method and device, a medium and equipment, and the method comprises the steps: determining the basic advertisement putting income of each Internet advertisement during each playing according to the audience of each Internet advertisement in an Internet advertisement list of an Internet advertisement system, and determining the putting path of each Internet advertisement; according to the basic advertisement putting income of each internet advertisement and a resource competition coefficient relative to other internet advertisements on the same path in the internet advertisement list, predicting an initial putting probability of putting each internet advertisement at a next putting time point on the putting path; correcting the initial putting probability according to the available playing duration and putting income of the next putting time point to obtain a corrected putting probability of putting each internet advertisement at the next putting time point; and according to the corrected putting probability, determining a target internet advertisement put at the next putting time point, and putting the target internet advertisement when the next putting time point is reached.

The invention relates to the technical field of online marketing, and discloses a marketing method for online sales, and the method comprises the steps: obtaining favorite commodities of a user, dividing a day into a plurality of time periods, and calculating the historical average browsing time of the user in each time period; judging whether the user opens shopping software or not in real time, and if the user opens the shopping software, obtaining a current time period and calculating the total number and total time of commodities browsed by the user so as to determine the browsing time of a single commodity; recommending a specific number of commodities from the intentional commodity set according to the single commodity browsing time and the average browsing time of the user in the current time period; and obtaining the browsing condition of the recommended commodity by the user in real time, and adjusting the subsequent recommended commodity according to the browsing feedback of the user.

The invention relates to the technical field of Internet, in particular to a multimedia content display method and device, electronic equipment and a storage medium, and aims to improve the click rate of multimedia content. The method comprises the following steps: presenting a content browsing interface; the content browsing interface is used for displaying the recommended information flow to the target object; in response to a content switching operation for the content browsing interface, presenting exclusive promotion content generated based on the target multimedia content carried by the information stream in the content browsing interface, and skipping to the exclusive promotion content and playing a content segment corresponding to the exclusive key information of the target level; the exclusive promotion content is used for describing different levels of exclusive key information matched with the target object; each piece of exclusive key information is comprehensively generated according to the original key information in the target multimedia content and the content preference information of the target object; the target level is determined according to the interestingness of the target object to the information flow.

The invention discloses a commodity module sorting method and device, electronic equipment and a storage medium, when a commodity module sorting request sent by a user is received, user feature data and a commodity module candidate set are obtained, and for each candidate commodity module in the commodity module candidate set, the commodity module sorting request is sent to the user; estimating a commodity conversion rate corresponding to each commodity in a commodity set of the candidate commodity module based on the commodity module sorting model and the user feature data, and selecting a target commodity conversion rate with a high conversion rate to determine a corresponding target commodity; and estimating the module conversion rate of the candidate commodity module to which each target commodity belongs, selecting the target module conversion rate with high conversion rate to determine the corresponding target commodity module, and outputting each target commodity module and the target commodity contained in each target commodity module. According to the invention, the commodity module and the commodity row preferred by the user can be displayed at the front position, and the accuracy of obtaining the commodity preferred by the user is improved.

The embodiment of the invention provides a method for displaying a service list and electronic equipment, and the method comprises the steps: receiving a first request message sent by first terminal equipment, the first request message comprises a first search term, and the equipment type and / or use scene of the first terminal equipment, the first request message is used for requesting to obtain a target service list matched with a first search term, and the equipment type and / or use scene of the first terminal equipment; determining a target service list from M service lists according to the first search term and the device type and / or usage scenario of the first terminal device, M being a positive integer greater than or equal to 1; and sending the target service list to a first terminal device, so that the first terminal device displays the target service list. Through the method and the electronic equipment, the service list acquired and displayed by the terminal equipment can be matched with the type and / or the use scene of the terminal equipment, the actual demand of a user is better met, and the click rate of the user is higher.

Disclosed are a search request processing method, a method for displaying search results, an apparatus for processing search requests, a search request processing device, a device for displaying search results, and a computer-readable storage medium. The present disclosure utilizes artificial intelligence technology to identify and delete unnecessary words in search requests to process search requests, thereby enabling the search engine to return more and more accurate search results. Compared with a method of randomly deleting a portion of search words in a search request, the core content in the search request can be retained, thereby improving the quantity and quality of search results that can be recalled by the search request.

The present disclosure provides a music resource recommendation method, program product and electronic device, and relates to the technical field of computers. The method comprises the following steps: obtaining associated corpus data of a target music resource, wherein the associated corpus data comprises text information related to the target music resource; inputting the associated corpus data into a large language model, performing semantic reasoning on the associated corpus data by the large language model, and generating an explanatory text corresponding to the target music resource, wherein the explanatory text is used to describe the content characteristics of the target music resource; encoding the explanatory text into a semantic vector; calculating the interest matching degree between the target music resource and a user based on the semantic vector and a historical behavior sequence of the user by using a recommendation model; determining target recommendation content recommended to the user according to the interest matching degree, and displaying the explanatory text corresponding to the target recommendation content as a recommendation reason. In this way, the cognitive load of the user can be reduced, and the recommendation effect of the music resource can be improved.

The invention provides a promotion information display method and device, equipment and a computer readable storage medium. The method comprises the steps of displaying promotion information in a first style in an information flow display page; and in response to the target operation, displaying the promotion information by adopting a dynamic switching style corresponding to the target operation, wherein at least one of display contents or display sizes of the promotion information corresponding to the first style and the dynamic switching style is different. According to the application, the display diversity of the promotion information can be realized, and the click rate and the conversion rate of the promotion information are improved.

The application provides an online sales click rate estimation method for agricultural products. The method of the application comprises the following steps: obtaining agricultural product sales related data, including product attributes, user behaviors, context information, etc., and cleaning and preprocessing the data; using a GBDT model to process the features, which can flexibly process various types of data without one-hot encoding processing of discrete features, introducing an Input-aware strategy to refine the weight vector and embedding vector of each feature for different instances, adaptively learning the influence of each input vector on feature representation, and adding a fully connected network to capture high-order features in a nonlinear manner, so that the method has better expression and generalization ability for complex structure data features.

The present invention relates to the technical field of online marketing, and discloses a marketing method for online sales, comprising: obtaining user's favorite products, dividing a day into multiple time periods, and calculating the user's historical average browsing time in each time period; judging in real time whether the user has opened a shopping software, and if the user has opened it, obtaining the current time period and calculating the total number and total time of the user's browsed products to determine the browsing time of a single product; recommending a specific number of products from a set of intended products according to the user's browsing time and average browsing time of a single product in the current time period; obtaining the user's browsing status of the recommended products in real time, and adjusting subsequent recommended products according to the user's browsing feedback.

The present application provides a personalized advertising push method and related devices based on product data. Obtain product data of the product to be promoted, collect basic information and sales data of the product to be promoted, and provide basic data for the subsequent recommendation algorithm. By identifying similar products, more choices can be provided to users to avoid single recommendations. At the same time, collecting similar product data can help the algorithm analyze user needs more accurately. The weight of each similar product is calculated by analyzing similar product data, which reflects the relevance of each similar product to the user's interests. Sort by weight coefficient, and select similar products corresponding to the first k weight coefficients as recommended products. This ensures the relevance and effectiveness of the recommended products. By displaying advertising information, users can easily obtain the latest recommendation information, improve the reach of advertising and user experience.

The invention belongs to the technical field of advertisement creativity, and discloses an activity advertisement creativity generation method, which specifically comprises the following steps of: 1, building an advertisement position style specification library, and maintaining all specifications of card styles of advertisement positions required to be put by a business system; according to the scheme, the generation efficiency of the advertisement creativity can be improved, personalized generation of the creativity is realized, the click rate and the conversion rate of the advertisement are improved, meanwhile, a creativity management function is provided, a user can conveniently manage and maintain the creativity, a business side constructs own basic services and creates own creativity generation tools, a creativity generation link is shortened, and the user experience is improved. External dependence and communication cost are reduced, creativity generation is more efficient, the system is more convenient to maintain, generation of personalized creativity of different crowds is supported, and creativity of thousands of people is achieved, creativity seen by different crowds is different, the effect is better, advertisement putting effect data are collected and fed back, and a high-quality creativity template is selected.

The invention relates to a method for dynamically generating a mobile banking activity set page, which comprises the following steps of: constructing a tree structure in which a root node represents an activity set page and a first-level node represents a service category, and dynamically generating child nodes according to marketing activity data, a DIEN model comprising an embedded layer, an interest extraction layer, an interest evolution layer and a prediction layer is used to predict the interestingness of the user to different marketing activities, and user interest prediction is realized; according to the method, the user geographic position, preference setting and interest prediction value are integrated, child node weights are calculated, a descending sorting algorithm is used for sorting the child node weights, activity set pages are dynamically arranged according to the sorting result, and personalized recommendation and efficient marketing are achieved. Through modular design and real-time dynamic updating, the system efficiency and the user experience are improved.

The invention discloses a recommendation method based on a parallel graph neural network in the technical field of intelligent recommendation, and the method comprises the steps: 1, distributing an initial embedding vector for each user, item and relation, and enabling the initial vector to be 64-dimensional; 2, inputting the user vector and the item vector in the step 1 into a lightweight graph convolution module to obtain the user vector and the item vector; and 3, inputting the user and item vectors output in the step 2 and the initialized relation and item vectors in the step 1 into an attention and graph convolutional network module to obtain the user and item vectors of the stage. And 4, repeating the steps 2 and 3 for L times to obtain a plurality of user and item vectors, and respectively weighting and combining the user and item vectors to obtain the user and item vectors of the stage. And 5, calculating the item and the user vector obtained in the step 4 to obtain the item and the user vector of the final stage. And 6, performing dot product operation on the user and item vectors obtained in the step 5 to obtain an interaction probability of the user to the item, and returning a recommendation result. The scheme is used for improving the recommendation quality and optimizing the user experience.

The embodiment of the invention belongs to the field of artificial intelligence, and relates to a product recommendation method, which comprises the following steps: obtaining a candidate product library, and obtaining user data of all users and product data of all candidate products; determining a candidate product corresponding to each user in a candidate product library according to a preset recall strategy; constructing an interaction matrix and a weight matrix according to the user data, calculating a user feature vector of each user according to the interaction matrix, the weight matrix and the user data, and calculating a product feature vector of each candidate product according to the interaction matrix, the weight matrix and the product data; calculating a matching degree between each user and each candidate product according to the obtained weight matrix, the user feature vector and the product feature vector, and determining a target product of each user based on the obtained matching degree; and according to the target product of each user, carrying out product recommendation on each user. The accuracy of product recommendation is improved.

The invention relates to the technical field of advertisement putting, and discloses a user behavior data-based advertisement picture putting method, which comprises the following steps of: acquiring behavior data of a target user, determining associated interests of the target user according to the behavior data of the target user, and determining an interest probability of the target user according to interest information of the target user; determining an associated interest of the target advertisement picture, and determining an interest probability of the target advertisement picture according to the associated interest of the target advertisement picture; according to the interest probability of the target user and the target advertisement picture, obtaining the matching degree of the target user and the target advertisement picture; constructing a game model based on the matching degree of the target user and the target advertisement picture, wherein the game model comprises participants, a strategy space and a revenue function; and determining a revenue balance point of the revenue function of the advertiser and the revenue function of the user according to a finite element accumulation optimization algorithm, and determining target advertisement picture putting according to the revenue balance point.

The present invention proposes an advertising delivery method and system based on user big data analysis. User information and advertising product information are obtained from a background database in real time; the user information includes user IP information, geographic location information and user identity information; the advertising product information includes product type information, product sales information, product browsing information and product search information; the advertising product information in the area where the user is located is obtained based on the user IP information and geographic location information, and the user identity information corresponding to the advertising product is obtained based on the user behavior data. By analyzing user big data, the user's interests, preferences, behavioral characteristics and other information can be deeply understood, thereby achieving accurate target user positioning. Advertisements can be delivered to users who are truly potentially interested, thereby improving the effect and conversion rate of advertising reach.