Communication service promotion platform based on artificial intelligence

Abstract

The invention discloses a communication service promotion platform based on artificial intelligence, and the platform comprises a background management system which is connected with a data collection and processing module, a user portrait construction module, an AI recommendation engine module, a promotion execution and optimization module, and a user feedback and interaction module. And the server is responsible for collecting feedback and suggestions of the user on the communication service, processing consultation and complaints of the user and providing a personalized solution. The invention belongs to the technical field of communication promotion, and can collect and process user data and construct a user portrait, generate a proper recommendation scheme through matching of the user portrait and a communication service, and then perform promotion execution and optimization on the recommendation scheme, thereby realizing accurate analysis of user behaviors and preferences, and improving user experience. Therefore, accurate matching and service customization of user demands are facilitated, a personalized communication service promotion scheme is provided, promotion efficiency and precision are improved, and user experience and satisfaction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication promotion, and in particular to a communication service promotion platform based on artificial intelligence. Background Art

[0002] In recent years, competition in the communications service market has become increasingly fierce. Major operators and Internet companies have launched new service products and marketing strategies to attract and retain users. However, traditional promotion methods often have problems such as low efficiency, high cost, and poor user experience, which are difficult to meet the rapidly changing needs of the modern market. Therefore, a more efficient and intelligent promotion platform is needed to meet market challenges.

[0003] With the popularization of mobile Internet and the widespread application of smart terminals, artificial intelligence (AI) technology has also made significant progress. In the field of communications, the application of artificial intelligence technology has not only improved network efficiency and optimized user experience, but also promoted the development and promotion of new services. With the increasing demand of enterprises and individuals for intelligent and personalized services, it is particularly important to design a communication service promotion platform based on artificial intelligence. Summary of the invention

[0004] To this end, the present invention provides a communication service promotion platform based on artificial intelligence to solve the above-mentioned problems in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A communication service promotion platform based on artificial intelligence, including a background management system, wherein the background management system is connected to:

[0007] Data collection and processing module, responsible for collecting user data and processing the data;

[0008] User portrait construction module, which constructs user portraits based on user data from the data collection and processing module, and regularly updates user portraits;

[0009] The AI ​​recommendation engine module is responsible for matching user profiles with the characteristics of current communication services and generating personalized communication service recommendation plans based on the matching results;

[0010] The promotion execution and optimization module is responsible for promoting the communication service recommendation solutions generated by the AI ​​recommendation engine module through various channels, and evaluating and optimizing the promotion effects;

[0011] The user feedback and interaction module is responsible for collecting users' feedback and suggestions on communication services, handling user inquiries and complaints, and providing personalized solutions.

[0012] As a further optimized solution of the present invention, the data collection and processing module includes:

[0013] The data collection unit is responsible for collecting the data generated by users in the process of using communication services, including user registration information, user behavior data, user preference data, and social data;

[0014] The data preprocessing unit is responsible for cleaning the data and formatting and standardizing the cleaned data;

[0015] The data classification and labeling unit is responsible for the classification of processed data and the generation of labels.

[0016] As a further optimized solution of the present invention, the user portrait construction module includes:

[0017] The portrait construction unit constructs a user portrait framework based on the label classification and generation results of the data collection and processing module, and fills the user data into the portrait framework to form a complete user portrait;

[0018] The portrait optimization unit updates the user portrait based on user feedback and new data, and regularly evaluates and optimizes the portrait to improve its accuracy and timeliness.

[0019] As a further optimized solution of the present invention, the AI ​​recommendation engine module includes:

[0020] The communication service matching unit is responsible for recommending the most suitable network type, service provider and package matching to users based on user profiles and network requirements;

[0021] Feature extraction unit, responsible for extracting key features from user behavior data and item information;

[0022] The model training unit is responsible for selecting the recommendation algorithm according to the application scenario and data characteristics, and training the recommendation model by using the extracted feature data, and converting the feature data into vector representation through the trained model;

[0023] The recommendation generation unit is responsible for indexing the trained vectors and related data, and then performing similarity search in the index based on the user's current behavior or historical data. It generates a recommendation list based on the search results, adjusts the recommendation results based on the user's preferences and historical behavior, and implements personalized recommendations.

[0024] As a further optimized solution of the present invention, the promotion execution and optimization module includes:

[0025] The multi-channel promotion unit is responsible for promoting the recommendation results generated by the AI ​​recommendation engine module through various channels;

[0026] Effect evaluation and optimization unit: Use testing methods to evaluate promotion effects and adjust promotion strategies based on feedback.

[0027] As a further optimized solution of the present invention, the user feedback and interaction module includes:

[0028] A user feedback collection unit, used to collect user feedback and suggestions on communication services;

[0029] Interaction management unit, used to handle user inquiries and complaints to provide personalized solutions.

[0030] The present invention has the following advantages:

[0031] By connecting the backend management system with the data collection and processing module, AI recommendation engine module, user portrait construction module, promotion execution and optimization module, and user feedback and interaction module, user data can be collected and processed and user portraits can be constructed. By matching user portraits with communication services to generate appropriate recommendation plans, and then the recommended plans are promoted, executed and optimized, an accurate analysis of user behavior and preferences is achieved to accurately match user needs and customize services, providing personalized communication service promotion plans, which is conducive to improving promotion efficiency and accuracy, thereby enhancing user experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a system block diagram of the present invention;

[0033] Figure 2 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0034] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] like Figure 1 As shown, a communication service promotion platform based on artificial intelligence includes a background management system, and the background management system is connected to:

[0036] Data collection and processing module, responsible for collecting user data and processing the data;

[0037] User portrait construction module, which constructs user portraits based on user data from the data collection and processing module, and regularly updates user portraits;

[0038] The AI ​​recommendation engine module is responsible for matching user profiles with the characteristics of current communication services and generating personalized communication service recommendation plans based on the matching results;

[0039] The promotion execution and optimization module is responsible for promoting the communication service recommendation solutions generated by the AI ​​recommendation engine module through various channels, and evaluating and optimizing the promotion effects;

[0040] The user feedback and interaction module is responsible for collecting users' feedback and suggestions on communication services, handling user inquiries and complaints, and providing personalized solutions.

[0041] Specifically, the data collection and processing module includes:

[0042] Data collection unit, responsible for collecting data generated by users in the process of using communication services, including user registration information (such as name, gender, age, occupation, region, contact information, etc.), user behavior data (such as browsing history, search history, click history, purchase history, usage time, etc.), user preference data (such as interests, consumption habits, brand preferences, etc.), social data (such as following, likes, comments, sharing, etc. on social media);

[0043] The specific collection methods are as follows:

[0044] 1. Collect data through user touchpoints such as websites and apps;

[0045] 2. Obtain data through third-party data providers;

[0046] 3. Collect data through user surveys, questionnaires, etc.

[0047] The data preprocessing unit is responsible for cleaning the data and formatting and standardizing the cleaned data;

[0048] The specific processing steps are as follows:

[0049] Data cleaning: remove duplicate data, invalid data, abnormal data, etc. to ensure data accuracy and consistency. For example, remove duplicate registered user information and clean up invalid contact information.

[0050] Data formatting: convert the cleaned data into a unified format to facilitate subsequent processing and analysis, for example, converting the user's age from a string to an integer type;

[0051] Data standardization: standardize the data to eliminate the dimensional differences between different data. For example, divide user income into different income ranges according to certain standards.

[0052] Data classification and labeling unit, responsible for the classification of processed data and the generation of labels;

[0053] The specific classification and label generation steps are as follows:

[0054] Establish tag types based on user data: including statistical tags (such as age, gender, region, etc.), rule tags (such as activity, purchasing power, etc.), and machine learning mining tags (such as user interest preferences, user value, etc.);

[0055] Activity can be generated based on user login and usage time, purchasing power can be generated based on user purchase amount and frequency, user interest preferences can be learned through user behavior data, and user value can be derived through comprehensive evaluation of user behavior, consumption, etc.

[0056] Generate labels: Generate statistical labels directly based on user registration information, generate rule labels based on user behavior data, and use machine learning algorithms to mine and analyze user preference data to generate machine learning mining labels.

[0057] Furthermore, the user portrait construction module includes:

[0058] The portrait construction unit constructs a user portrait framework based on the label classification and generation results of the data collection and processing module, and fills the user data into the portrait framework to form a complete user portrait;

[0059] For example, build a user profile that includes tags such as user age, gender, region, activity, purchasing power, interest preferences, etc.

[0060] The portrait optimization unit updates the user portrait based on user feedback and new data, and regularly evaluates and optimizes the portrait to improve its accuracy and timeliness;

[0061] For example, based on the user's new purchase records and behavior data, update the user's purchasing power and interest preference labels, and adjust the portrait framework and labeling system based on market trends and user feedback.

[0062] Embodiment 1

[0063] Assuming that the communication service provider uses an e-commerce platform for promotion, we need to build a user profile of the e-commerce platform. The specific steps are as follows:

[0064] S1 Data Collection: Collect user registration information (such as name, gender, age, region, etc.), browsing history, purchase history, evaluation history, etc.;

[0065] S2 data processing: clean the user data, remove duplicate and invalid records, convert the age from a string to an integer, and divide the user income into different income ranges;

[0066] S3 tag generation and classification:

[0067] Statistical tags: age (25 years old), gender (female), region (Beijing);

[0068] Rule-based tags: high activity and strong purchasing power;

[0069] Machine learning mining tags: interest preferences (clothing, beauty);

[0070] S4 portrait construction: Build a user portrait framework through labels such as age (25 years old), gender (female), region (Beijing), high activity, strong purchasing power, and interest preferences (clothing, beauty), and then fill the user data into the portrait framework to form a complete user portrait;

[0071] S5 Portrait Optimization: Update the user's purchasing power and interest preference labels based on the user's new purchase records and behavior data, and then adjust the portrait framework and label system based on market trends and user feedback to achieve regular evaluation and optimization;

[0072] Through the above steps, we can build a complete and accurate user portrait to provide strong support for subsequent precision marketing and service customization.

[0073] Specifically, the AI ​​recommendation engine module includes:

[0074] The communication service matching unit is responsible for recommending the most suitable network type, service provider and package matching to users based on user profiles and network requirements;

[0075] It should be noted that the data collection and processing module will collect the following network demand information:

[0076] Device type: such as smartphone, laptop, tablet, etc.) This information helps understand the communication method that the user may use;

[0077] Usage habits: For example, whether the user often uses the Internet at home or outside, which will affect the recommendation of network connection methods;

[0078] Network requirements: The user's demand for network speed, whether high-speed download or upload is required, which determines the recommended network type (such as 4G / 5G, Wi-Fi, fiber broadband, etc.);

[0079] Budget constraints: the amount the user is willing to pay for communication services, which helps filter out service options that fit the budget;

[0080] The communication service matching unit will match the above network demand information with the user profile, and will also analyze the network infrastructure in the user's area, the availability and coverage of different network connection methods, etc., to ensure that the recommended communication service is feasible in the local area;

[0081] After the user selects the recommended communication service, the user feedback and interaction module will collect user feedback, such as connection speed, stability, satisfaction, etc. These feedbacks will be used to optimize the recommendation algorithm to more accurately meet user needs. Through continuous optimization and learning, the AI ​​recommendation engine module can continuously improve the accuracy and efficiency of recommendation services;

[0082] The feature extraction unit is responsible for extracting key features from user behavior data and item information. It is specifically divided into user feature extraction and item feature extraction. When extracting user features, statistical methods or machine learning algorithms can be used to analyze user behavior and extract key features such as user preferences, interests, and activity from user behavior data. When extracting item features, text mining technology can be used to extract key features such as item category, attributes, and price from item information.

[0083] The model training unit is responsible for selecting recommendation algorithms (such as collaborative filtering, content-based recommendation, hybrid recommendation, etc.) according to the application scenario and data characteristics, and training the recommendation model by using the extracted feature data. Machine learning algorithms such as TF-IDF model and Word2Vec model can be used to convert feature data into vector representation.

[0084] The recommendation generation unit is responsible for indexing the trained vectors and related data, such as using search engine technologies such as Elasticsearch, and then performing similarity searches in the index based on the user's current behavior or historical data. A recommendation list is generated based on the search results, which may include item recommendations, content recommendations, etc. The recommendation results are adjusted based on the user's preferences and historical behaviors to achieve personalized recommendations. Mechanisms such as time decay factors and personalized recommendation weights can also be introduced to improve the accuracy and timeliness of recommendations.

[0085] Embodiment 2

[0086] Assuming that a communications service provider uses an e-commerce platform for promotion, we need to use the AI ​​recommendation engine module to make personalized product recommendations. The specific steps are as follows:

[0087] S1 Data Collection: Collect users’ browsing history, purchase history, and search history;

[0088] S2 cleans data: remove invalid, duplicate or abnormal records;

[0089] S3 data formatting: convert browsing time and purchase amount into a unified format;

[0090] S4 feature extraction: extract the user's favorite product categories and brands from user behavior data, and extract the product category, price and brand from product information;

[0091] S5 model training: Select collaborative filtering algorithm as the recommendation algorithm, use the extracted feature data to train the collaborative filtering model, and calculate the similarity between users and products;

[0092] S6 recommendation generation: Use Elasticsearch to create a product index, perform similarity search in the index based on the user's current behavior or historical data, generate a product recommendation list based on the search results, and display it to the user;

[0093] Through the above steps, the present invention can provide personalized product recommendation services for users.

[0094] Specifically, the promotion execution and optimization module includes:

[0095] The multi-channel promotion unit is responsible for promoting the recommendation results generated by the AI ​​recommendation engine module through various channels, including social media, advertising platforms, emails, text messages, etc.

[0096] Effect evaluation and optimization unit: Use testing methods to evaluate promotion effects and adjust promotion strategies based on feedback. The preferred testing method is A / B testing method.

[0097] Specifically, the user feedback and interaction module includes:

[0098] A user feedback collection unit, used to collect user feedback and suggestions on communication services;

[0099] Interaction management unit, used to handle user inquiries and complaints to provide personalized solutions.

Claims

1. A communication service promotion platform based on artificial intelligence, characterized in that: Including a backend management system, the backend management system is connected with: Data collection and processing module, responsible for collecting user data and processing the data; User portrait construction module, which constructs user portraits based on user data from the data collection and processing module, and regularly updates user portraits; The AI ​​recommendation engine module is responsible for matching user profiles with the characteristics of current communication services and generating personalized communication service recommendation plans based on the matching results; The promotion execution and optimization module is responsible for promoting the communication service recommendation solutions generated by the AI ​​recommendation engine module through various channels, and evaluating and optimizing the promotion effects; The user feedback and interaction module is responsible for collecting users' feedback and suggestions on communication services, handling user inquiries and complaints, and providing personalized solutions.

2. The communication service promotion platform based on artificial intelligence according to claim 1, characterized in that: The data collection and processing module includes: The data collection unit is responsible for collecting the data generated by users in the process of using communication services, including user registration information, user behavior data, user preference data, and social data; The data preprocessing unit is responsible for cleaning the data and formatting and standardizing the cleaned data; The data classification and labeling unit is responsible for the classification of processed data and the generation of labels.

3. The communication service promotion platform based on artificial intelligence according to claim 2, characterized in that: The user portrait building module includes: The portrait construction unit constructs a user portrait framework based on the label classification and generation results of the data collection and processing module, and fills the user data into the portrait framework to form a complete user portrait; The portrait optimization unit updates the user portrait based on user feedback and new data, and regularly evaluates and optimizes the portrait to improve its accuracy and timeliness.

4. The communication service promotion platform based on artificial intelligence according to claim 1, characterized in that: The AI ​​recommendation engine module includes: The communication service matching unit is responsible for recommending the most suitable network type, service provider and package matching to users based on user profiles and network requirements; Feature extraction unit, responsible for extracting key features from user behavior data and item information; The model training unit is responsible for selecting the recommendation algorithm according to the application scenario and data characteristics, and training the recommendation model by using the extracted feature data, and converting the feature data into vector representation through the trained model; The recommendation generation unit is responsible for indexing the trained vectors and related data, and then performing similarity search in the index based on the user's current behavior or historical data. It generates a recommendation list based on the search results, and adjusts the recommendation results based on the user's preferences and historical behavior to achieve personalized recommendations.

5. The communication service promotion platform based on artificial intelligence according to claim 1, characterized in that: The promotion execution and optimization module includes: The multi-channel promotion unit is responsible for promoting the recommendation results generated by the AI ​​recommendation engine module through various channels; Effect evaluation and optimization unit: Use testing methods to evaluate promotion effects and adjust promotion strategies based on feedback.

6. The communication service promotion platform based on artificial intelligence according to claim 1, characterized in that: The user feedback and interaction module includes: A user feedback collection unit, used to collect user feedback and suggestions on communication services; Interaction management unit, used to handle user inquiries and complaints to provide personalized solutions.

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