1467 results about "E-commerce" patented technology

The invention relates to the technical field of safety certification, in particular to an electronic commerce platform safety certification system and method. The method comprises the following steps: acquiring platform login identity information, performing field migration and recombination, constructing an identity information tracing chain, performing multi-domain splitting on the tracing chain, generating a domain segmentation identity chain, performing identity risk assessment based on the multi-domain splitting, generating risk parameters, and performing parallel fusion authentication according to the risk parameters and the domain segmentation identity chain. The method comprises the steps of generating an initial security authentication result, logging in through a disguise platform, collecting login environment change data, verifying environment authenticity and performing security judgment to obtain login environment security data, performing platform login re-authentication by utilizing the security data and the initial authentication result, updating a login state and establishing a repeated authentication mechanism. According to the invention, a set of more comprehensive security protection system is formed through a multi-level security authentication strategy.

The invention relates to the technical field of service examination and approval, and discloses a service examination and approval processing method, device and equipment and a storage medium, and the method comprises the steps: receiving a user authorization request, verifying the user authorization request, and obtaining a sub-account certificate with a reading only permission; performing real-time data acquisition on the plurality of e-commerce platforms based on the sub-account credentials to obtain multi-dimensional user data including transaction records, operation data and evaluation information; inputting the multi-dimensional user data into a preset space-time sequence attention network and graph convolution hybrid model to carry out dynamic evaluation on a user behavior pattern to obtain risk quantitative indexes; inputting the risk quantitative indexes into a rule engine integrating an adaptive decision tree and a genetic algorithm for automatic decision making to obtain a graded approval decision; and performing multi-signature encryption on the execution instruction according to the graded approval decision to obtain a safety operation instruction and execution state information, thereby realizing full-process automation from data acquisition, risk assessment to decision execution.

The invention relates to the technical field of shopping guide, in particular to a multi-mode user intention understanding and personalized shopping guide generation method and system, and the method integrates vision, voice and touch screen data to construct a commodity and user intention knowledge graph. Through a cross-modal contrast learning architecture and deep feature learning of user intentions, precise commodity matching and sorting are realized; and in combination with a knowledge-enhanced semantic alignment model, generating a personalized recommendation verbal skill, and making a shopping guide strategy by using a sequence-to-sequence neural network. Complementary information among different modal data can be captured, a unified semantic representation space can be learned, and the accuracy of user intention understanding is greatly improved; the system breaks through the technical barriers, significantly improves the intelligent shopping guide capability of the e-commerce platform, rebuilds the shopping experience of the user, shows huge commercial value, and injects new power for industry development.

The invention relates to the technical field of data desensitization, in particular to an electronic commerce platform information security desensitization scheme analysis system based on artificial intelligence, which comprises a data classification module, an access evaluation module, a dynamic desensitization module and a security audit module. According to the method, the data of the e-commerce platform is intelligently classified and evaluated by adopting an artificial intelligence technology, dynamic desensitization and finer access control of the data are realized, various data access requirements can be dynamically met by evaluating the sensitivity level of the file and the security level of the access request, data processing is performed according to requirements, and the data processing efficiency is improved. The security and privacy of user data are effectively protected, through automation of security audit, the recognition efficiency of abnormal behaviors is improved, the transparency and traceability of overall data management are enhanced, through real-time analysis and response, the risk of data leakage is reduced, and the practical value of data and the fluency of service operation are kept.

The present disclosure relates generally to computer-implemented systems and techniques for leveraging a virtual decentralized database to enable user identity authentication and verification of user activity and associated behaviors on open internet and public web-based and native applications. Techniques are disclosed for user identification profile onboarding using biometrics, frictionless CAPTCHA (Completely Automatic Public Turing Tests To Tell Humans And Computers Apart) processes, passive data capture of physiological and behaviors using device sensors, and cryptographic methods of storing user engagement activity as immutable data files for secure distribution on a decentralized virtual private network. Such technologies operate to establish a high confidence level for user authenticity, communication origin, engagement outcome transparency and clarity of intent for users active on social, commercial, e-commerce and information sharing platforms. More specifically, an unobtrusive passive data capture method of authentication and verification on a secure VPN supports the organic, spontaneous and original experience audiences desire while simultaneously eliminating opportunities for unscrupulous actors to exploit open internet exchanges by inserting fraudulent user accounts, automated bots and other measures that mimic or impersonate live humans, which ultimately undermines user confidence in data safety and erodes mutual trust between people, companies and institutions.

The invention discloses an electronic commerce inventory intelligent optimization method based on big data, and relates to the technical field of big data, and the method comprises the steps: inputting a Pareto optimal solution set into a Mean-Field multi-agent game model and a federal learning framework coordination node strategy, and generating an optimization strategy set containing an elastic safety interval and emergency pairing allocation; optimizing a strategy-driven autonomous evolution type performance mechanism, matching commodity nodes with warehouse nodes, triggering re-routing, and generating a dynamic transport capacity scheduling scheme including AGV collaborative transportation, unmanned aerial vehicle emergency distribution and carbon emission decomposition; the dynamic transport capacity scheduling scheme triggers multi-resolution digital twinborn verification, microscopic discrete event simulation and macroscopic agent model collaborative evaluation are carried out, a simulation verification report is generated, and a multi-resolution digital twinborn verification mechanism carries out multi-resolution digital twinborn verification through collaborative optimization of microscopic discrete event simulation and macroscopic agent rules. And the transport path priority and carbon emission budget allocation are dynamically corrected, and the performability of the elastic strategy set is ensured.

The invention belongs to the technical field of e-commerce, and particularly relates to a big data-based recommendation algorithm design in an e-commerce platform, which comprises the following steps: completing data preprocessing based on data collection and preprocessing; constructing user portraits to generate personalized user portraits, and classifying the users by using a clustering algorithm to find group features of the users; analyzing the user similarity and the commodity similarity, and calculating the similarity between the users; carrying out user-based collaborative filtering and article-based collaborative filtering, and carrying out dimensionality reduction processing on user and commodity features by using an SVD or latent semantic model; modeling user behaviors by using a neural network, and performing data compression and feature extraction by using an auto-encoder; the user feedback data is collected for continuous optimization and iteration of the model, and online learning and A / B test are adopted to adjust the recommendation strategy, so that the method has the effects of realizing more accurate and personalized commodity recommendation, and improving the shopping experience of the user and the sales conversion rate of the platform.

A system for monitoring and optimizing the performance of an e-commerce platform, which includes the following: a) a data collection and integration module configured to retrieve real-time data from e-commerce platforms, marketing channels, inventory systems and user interactions using APIs, tracking scripts and server-to-server communication; b) a data preprocessing and filtering module for cleaning and structuring the collected data by removing inconsistencies, normalizing formats and detecting outliers; c) a machine learning and predictive analytics machine that uses deep learning, regression models and clustering algorithms to analyze historical and real-time data to predict sales trends, customer behavior and demand fluctuations; d) a performance monitoring dashboard configured to visualize key performance indicators, sales trends and marketing effectiveness, and to provide custom reports and real-time alerts; e) an automated module for optimizing marketing campaigns, which dynamically adjusts advertising strategies, budget allocations, target audience approach and creative elements based on real-time data insights; f) an automated decision and control system configured to execute AI-driven optimization strategies, including bid adjustments, price changes and inventory reallocations without manual intervention; (g) a fraud detection and compliance monitoring module that uses anomaly detection algorithms to identify fraudulent transactions, fake reviews and advertising fraud while ensuring compliance with legal regulations; the system continuously monitoring, analyzing and optimizing the performance of the e-commerce platform through automated data-driven decision-making.

The invention relates to the technical field of e-commerce data processing, in particular to an e-commerce sales platform background data management method and system. The method comprises the following steps: S1, collecting a commodity dynamic data stream, a user behavior event stream and a promotion strategy stream in real time, and generating a standardized data stream through time sequence alignment; s2, constructing a dynamic coupling data cube; s3, executing a real-time decision: in response to the payment request, selecting an inventory distribution or replacement commodity pushing strategy based on a commodity-user coupling matrix value; in response to the resource overload state, triggering a resource scheduling strategy; and S4, dynamically adjusting calculation parameters of the coupling matrix according to decision execution feedback. By solving the problems of real-time standardization processing of multi-source heterogeneous data and real-time coupling of dynamic inventory and user behaviors, the data processing capacity, inventory distribution efficiency and recommendation accuracy of the platform are remarkably improved.

Exemplary embodiments include systems and methods for generating dynamic questions and answers for a customer of an ecommerce store, the systems and methods comprising: a database storing context information regarding products listed on the ecommerce store; a user interface element supported by the ecommerce store, configured to receive a query and further configured to populate an answer to the query and a predicted follow-up question; a server coupled to the user device; and a large language model coupled to the source database and the server. The large language model is configured to: generate an initial set of product-related questions using context information for products listed on the ecommerce store; receive the query; prepare a contextual response to the query using a pre-configured prompt, the query, and the information regarding the product stored in the at least one source database; and generate and display the contextual response and predicted follow-up question.

The invention relates to the technical field of e-commerce marketing, in particular to a product marketing service platform system and method, and the system comprises a user behavior tracking module, a consumption intention analysis module, a promotion time optimization module, an inventory monitoring and discount adjustment module, and a precision marketing push module. According to the method, the commodity access frequency and cross browsing data are extracted and analyzed in real time, the consumption trend of the user in a short period is rapidly identified, consumption tendency parameters are generated, potential purchase behaviors are accurately predicted by using browsing behaviors and an interaction mode, the recommendation accuracy is improved, the real-time inventory data are combined, a discount strategy and inventory management are dynamically adjusted, and the recommendation efficiency is improved. According to the method, the promotion activity is ensured to be closely matched with the inventory state, the resource utilization efficiency is improved, precise marketing pushing is performed according to the user equipment preference, the user interaction experience is enhanced, the sales growth is promoted, and the market response speed and the marketing effect are remarkably improved.

The invention relates to the technical field of e-commerce, in particular to an e-commerce promotion method and system based on cloud computing, and the method comprises the following steps: building a user behavior sequence through log collection, extracting a sudden change point through dynamic time warping, generating a dynamic parameter through segmented integral, calculating a difference degree through gray correlation analysis, and reconstructing a weight set. And flexibly scheduling advertisement resource distribution, and generating a delivery instruction. According to the method, user click stream data is collected, a behavior sequence model is constructed, continuous dynamic characteristics of user browsing tracks are captured, time sequence relevance is analyzed, fluctuation peak points of user behavior paths are aligned, interest mutation nodes are captured, abnormal behavior modes are recognized, and implicit preferences and explicit feedback parameters in the interaction process are quantified. Establishing an associated feature analysis mechanism, resetting advertisement weight parameters, matching real-time requirements of users with putting strategies, mapping weights to distributed advertisement nodes, dynamically scheduling computing resources, adapting display requirements and resource allocation, and forming a closed-loop optimization link.

The invention discloses an e-commerce personalized recommendation method based on cross-domain collaborative filtering, and relates to the technical field of e-commerce, and the method comprises the steps: collecting and preprocessing data, including user behavior data and commodity attribute data, of different fields of an e-commerce platform; materializing the collected data in different fields, and integrating the data into a structured e-commerce knowledge graph; and constructing a user-based cross-domain collaborative filtering model based on the behavior data of the user in the multiple fields, and predicting commodities in which the user is interested. By integrating data in different fields and constructing user portraits and commodity portraits, the problem of inaccurate recommendation caused by data heterogeneity in a traditional recommendation system is effectively solved, interests and preferences of users in different fields can be more accurately captured through cross-domain data association analysis, and the recommendation efficiency is improved. Therefore, personalized recommendation better meeting the requirements of the user is provided for the user, the recommendation accuracy and satisfaction degree are remarkably improved, and the user stickiness and the platform competitiveness are enhanced.

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

The invention relates to a method for intelligently pushing commodity display according to user behavior habits, and belongs to the technical field of artificial intelligence and electronic commerce recommendation systems. Aiming at the problems of low recommendation accuracy and insufficient real-time performance caused by dynamic change of user behaviors in the existing commodity pushing technology, the method comprises the following steps of: acquiring multi-dimensional data such as user browsing tracks, click preferences, purchase records and page staying duration, and constructing a dynamic user portrait in combination with a time sequence analysis and clustering algorithm; fusing real-time behavior feedback by adopting an improved collaborative filtering algorithm, mining potential association between behavior characteristics and commodity attributes through a deep learning model, and establishing an adaptive weight adjustment mechanism; and finally, a personalized commodity sorting strategy is generated based on the current scene and behavior trend prediction of the user, and dynamic optimization of the pushed content is realized. The method can be applied to an e-commerce platform, an advertisement putting system and a mobile application, the recommendation accuracy, the user conversion efficiency and the platform sales volume are remarkably improved, and meanwhile computing resource consumption is reduced.

The invention provides an inventory management method for e-commerce, which integrates links of multi-source data analysis and prediction, intelligent replenishment, dynamic inventory tracking and return management, constructs a demand prediction model by using historical sales records, user behaviors and market dynamic information, sets a safe inventory level based on a prediction result, and provides an inventory management method for e-commerce. An automatic purchase request and a quick response replenishment mechanism are implemented, real-time inventory synchronization is achieved through an API, inventory changes are monitored through the Internet of Things technology, a commodity processing decision is guided for a returned commodity development prediction model, unsalable commodities are regularly examined, products close to the expiration date are reasonably handled, and a KPI is set and comprises the inventory turnover rate and the order accuracy rate. And closed-loop control of inventory management is realized.

An AI-based automated pricing and inventory management system (100) for retail marketplaces, including: a data collection module configured to collect real-time data from sales transactions, competitor pricing, market trends, inventory levels, and customer behavior; an AI engine configured to process the collected data using machine learning algorithms for demand forecasting, price optimization, and customer segmentation; a price optimization module that is communicatively connected to the AI ​​machine and configured to dynamically adjust product prices based on demand patterns, competitive strategies and predefined business rules in order to maximize profitability and maintain competitiveness in the market; an inventory management module configured to predict optimal stock levels, generate replenishment alerts, and recommend inventory reduction strategies, thereby minimizing overstocking and stockouts; a dashboard interface configured to display actionable insights, pricing strategies, inventory levels, and key performance indicators in real time; an integration layer configured to interface with existing retail systems (100), including point-of-sale (POS), enterprise resource planning (ERP) and e-commerce platforms, enabling seamless operation and data exchange; the system dynamically coordinates (100) decisions on pricing and inventory management based on real-time data analysis, thereby improving operational efficiency, profitability and customer satisfaction.

Implementations of the present disclosure provide receiving, by a conversational commerce optimizer system, a user input from a user, the user input being provided to a chatbot of an e-commerce platform, providing a refined query based on the user input, selecting, by the conversational commerce optimizer system, a foundation model that is to be queried using the refined query, determining a set of search results from a database that stores data representative of products available through the e-commerce platform, prompting the foundation model using a prompt that is generated based on the refined query and the set of search results, the foundation model providing an output responsive to the prompt, and providing a response to the user through the user interface of the chatbot, the response being generated based on the output.

The invention discloses a quick retrieval method for e-commerce information, and relates to the technical field of e-commerce information.The method comprises the steps that features are extracted, multi-modal feature learning is conducted through image-text semantic matching and high-order semantic fusion, and commodity retrieval efficiency and accuracy are optimized through hash coding; analyzing and structuring user query through natural language processing and a deep semantic reasoning technology, and extracting a user intention in combination with numerical reasoning and logic processing; commodity matching is accelerated through Hash coding and approximate nearest neighbor retrieval, a retrieval result is optimized in combination with image-text semantic fusion and multi-factor sorting, and recommendation accuracy and personalization are continuously improved through a user feedback mechanism; the interpretability of a result is enhanced through reasoning chain echoing, and commodity recommendation is dynamically optimized by utilizing user feedback and reinforcement learning. According to the technical method, a more efficient, accurate and personalized commodity retrieval scheme is provided for the e-commerce platform, and the technical method has a wide application prospect.

The invention discloses a live broadcast e-commerce compliance auditing method and system based on penetration type supervision, and relates to the technical field of e-commerce compliance supervision, and the method comprises the steps: carrying out the Hash abstract calculation of a judgment result data set and a multi-source data set, carrying out the partitioning storage of the data set in a block chain, verifying the storage process through a timestamp and a multi-node consensus protocol, and carrying out the verification of the verification result; forming an evidence storage data chain; automatically matching the evidence storage data chain with a compliance rule, identifying potential illegal behaviors and risk levels, and forming a compliance result set; performing fusion calculation on the compliance result set, historical violation records and user feedback data to generate a comprehensive risk score, and automatically triggering real-time compliance intervention measures according to the comprehensive risk score to form an intervention result set; and re-storing the intervention result set, the comprehensive risk score and the violation behavior in the block chain to form a closed-loop evidence storage data chain. According to the invention, supervision transparency is enhanced, data integrity is ensured, and reliable support is provided for real-time intervention and closed-loop auditing.

An AI-powered augmented reality (AR) system for optimizing product placement and customer loyalty. The system includes: • a user interaction data collection unit configured to monitor consumer behavior in real time in any retail scenario (point-of-sale POS, online e-commerce and shopping areas within a public virtual technology space), with the unit analyzing consumer movement patterns, engagement metrics, eye-tracking and purchase intent; • an intelligent spatial mapping module to build a contextual understanding of high-traffic zones, underutilized areas and high-quality product placements using heatmap analysis, other spatial computer algorithms and the live movement of consumers; • A model-based product placement recommender based on machine learning, which uses one or more DL-based predictive models trained by importing current sales data, historical text interaction data, and consumer preference data to suggest optimal product placement so that customers see the advertisement and are likely to click through it to ensure a conversion. • a customer-centric, real-time AR visualization interface configured to allow customers to experience digitally enhanced product presentations directly via mobile AR applications, smart glasses, or in-store digital kiosks, enabling customers to preview product placements, rotate and resize objects, and visualize interactive advertising content before purchasing; • a customized advertising and sales promotion engine configured to generate augmented reality-based marketing content and provide users with tailored promotions and interactive ads based at least in part on an analysis of consumer sentiment by the system, as well as information on location data or consumer interaction history, wherein the customized advertising and sales promotion engine personalizes the configuration instructions for advertising and sales promotion to encourage consumer interaction and achieve higher sales revenues; • A secure, cloud-based data processing and analytics unit that uses an automated engagement model to link online and in-store sales information with established activity models for product placement decisions. The unit leverages AI-powered consumer insights to continuously recommend product placement changes based on a variety of programmatic retail trends; • a privacy-friendly AI module based on federated learning and on-device AI processing, providing consumer behavior analytics while complying with global data protection regulations and preventing the use of open data; • a processing framework characterized by a low-power mode suitable for AR rendering and AI calculations, and implemented via dynamic voltage scaling and energy management techniques for seamless and sustainable operation; The system according to claim 1, wherein the user interaction data capture unit further comprises an eye-tracking and sentiment analysis sub-module, wherein the sub-module detects facial expressions, emotional reactions, and attention levels of consumers in real time to determine the effectiveness of product placements and advertising strategies.

System, process, and method for gamifying physical assets, digital assets, and virtual assets through advertising and e-commerce, and system and method for personalized digital twin LLM chatbot gamification in e-commerce and emotional intelligence development. The first aspect of the invention is a system and process for creating and training a personalized digital twin LLM chatbot assistant. The digital twin is personalized for a given human user using training data regarding the human user. A second aspect of the invention is a system and process of creating and playing an e-commerce game, including the mechanics of the game. The e-commerce game may be created and / or played by employing personalized digital twin LLM chatbot assistant such as that described in the first aspect of this invention. State of the art technologies such as sensors, IoT devices, wearable devices, and VR / XR / AR can be integrated into the game.

A camping wagon achieves high portability and large capacity. Meanwhile, the camping wagon features lower sales costs and more cost-effective packaging in the e-commerce field. In the camping wagon, each of four corners of a folding bottom frame is provided with one support rod; one first folding side frame is provided between each two adjacent support rods in a width direction of the folding bottom frame, and one second folding side frame is provided between each two adjacent support rods in a length direction of the folding bottom frame; upper side ends of two second X-shaped movable frames of the second folding side frame are respectively hinged to corresponding support rods through linkage rods; and two upper side ends of one third X-shaped movable frame of the first folding side frame are respectively hinged to corresponding support rods through linkage rods.

The invention provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

The invention relates to the technical field of transaction verification, in particular to an electronic commerce transaction verification system based on a block chain, and the system comprises a transaction identity verification module, a fund flow monitoring module, an order payment state confirmation module, an abnormal transaction comparison module and a risk control strategy adjustment module. According to the invention, identity verification is carried out by using the hash calculation of the timestamp, the device identifier and the user public key and the block chain technology, so that the authorization and override accuracy of the transaction request is ensured, dynamic monitoring and trend analysis are carried out on the fund flow direction, including the measurement of the fund flow path and the evaluation of the fund transfer change rate, and the fund transfer accuracy is improved. The method effectively monitors the stable flow condition of funds, improves the recognition capability of transaction abnormity through integrating multi-source data analysis, enhances the real-time performance and accuracy of risk management, improves the safety and transparency of transaction, enhances the rapid response capability of abnormal behaviors, and improves the safety and reliability of the transaction. And higher-level trust and security assurance are brought to the user and the platform.

The invention relates to the technical field of electronic commerce dynamic management, and particularly discloses an electronic commerce management system and method based on big data, and the method comprises the steps: collecting the data of each commercial operation node of an electronic commerce platform, and carrying out the real-time collection and time serialization processing of the data; the transmission quantity, the flow direction and the interaction frequency of the collected data are analyzed, interaction modes among different operation nodes are monitored, and potential abnormal interaction nodes are identified; based on the abnormal interaction node, calculating a failure node coefficient for judging whether the node has a failure risk; for non-failure nodes, constructing a multi-dimensional risk assessment model, predicting future risks of the nodes by adopting a random forest algorithm, and calculating risk coefficients; through comprehensive analysis of the node abnormal coefficient, the failure node coefficient and the risk coefficient, priority early warning and emergency processing are performed on the high-risk node, so that the operation risk is effectively prevented, and the stability and the response capability of the platform are improved.

The invention relates to the technical field of e-commerce, and particularly discloses an intelligent customer service implementation system and method based on a digital human, which utilizes a natural language processing technology based on deep learning to perform semantic analysis on commodity information and interaction content input by a user. And mining semantic feature expressions of the user interaction information and each piece of commodity information, determining the commodity information required by the user based on the semantic similarity between the user interaction information and each piece of commodity information, and returning corresponding commodity introduction information. In this way, more personalized and efficient customer service can be provided for the user, so that the user experience and the service efficiency of the customer service system are improved.

The invention discloses an electronic commerce product live broadcast selling voice monitoring method and system, and relates to the technical field of voice monitoring, and the method comprises the steps: carrying out the sound source separation of a live broadcast audio stream, extracting an anchor sound track, and generating a structured semantic unit; constructing a knowledge graph, calculating cosine similarity between entities in the semantic unit and nodes of the knowledge graph, updating the knowledge graph, and calculating illegal path propagation probability; dynamically adjusting a time window according to the entity density of the semantic units, aggregating the semantic units in the window to generate event objects, detecting associated event pairs, and combining to form an event chain; according to the method, the comprehensive risk value of the current live broadcast is calculated through the maximum violation path propagation probability, the total duration and the audience report frequency of the event chain, the risk level is divided according to the comprehensive risk value, and the hierarchical response measure is executed, so that the problems of difficulty in hidden violation verbal skill identification and weak cross-time violation element association in e-commerce live broadcast selling are solved, and the user experience is improved. And the accuracy and integrity of illegal behavior monitoring are obviously improved.

A system for personalized product recommendations in an e-commerce platform, comprising: a user data collection module configured to collect browsing history, purchase records, search queries, and demographic information; a data preprocessing module configured to apply cleaning, feature extraction, and natural language processing (NLP) techniques to structure raw data; a product information management module configured to extract, classify, and update product attributes using NLP and image recognition; a recommendation engine that uses machine learning models, including collaborative filtering, content-based filtering, and deep learning techniques, configured to generate personalized product recommendations; a contextual filtering module configured to dynamically refine recommendations based on real-time factors such as user location, device type, and market trends; a feedback learning module configured to continuously update recommendation models using user ratings, reviews, and engagement metrics; and an API integration module configured to enable seamless connectivity with third-party e-commerce applications, inventory management systems, and analytics tools.

The invention relates to the field of risk management of e-commerce transaction behaviors, in particular to an e-commerce transaction behavior risk management method and a storage medium. The method comprises the following steps: collecting transaction data, analyzing the transaction data by using a transaction behavior analysis model based on a deep residual network (ResNet) and an attention mechanism, and identifying an abnormal transaction mode; in combination with a natural language processing (NLP) technology, performing sentiment analysis on user comments, social media and news, and predicting a transaction risk; an analysis result is input into a smart contract based on the block chain, a transaction rule is automatically executed, and transaction transparency and security are improved; a distributed account book technology of a block chain is utilized to ensure that transaction records cannot be tampered, and data security is enhanced; and a multi-mode biological recognition technology is adopted, so that the accuracy and safety of user identity verification are improved.