User personas are an important tool in digital marketing, which can help product managers and operators understand the needs, preferences and behaviors of users, so as to provide more personalized and high-quality services. This paper introduces the user portrait generation method, including three steps: feature extraction, model training and user portrait generation, and focuses on the role of artificial intelligence large models in these steps. Large models refer to Xi models with ultra-large-scale parameters and data, which can exhibit strong generalization ability and creativity across multiple domains and tasks. This paper will show how large models can improve the quality and diversity of data in feature extraction, improve the performance and efficiency of models in model training, and improve the accuracy and practicability of user portraits in user portrait generation. This topic will also describe how to manage user portraits, including how to update, maintain, and apply user portraits. Finally, this article will recommend a column "Using AI to Drive Digital Marketing Performance Growth", which is created by product design experts and business experts "Product Manager Dugu Shrimp" (the same number on the whole network), aiming to help product managers and operators in corresponding industries and fields master the principles and applications of large models, and improve the effectiveness and competitiveness of digital marketing.
How to generate user portraits
User portrait is the process of labeling and classifying the user's basic information, interests, hobbies, consumption Xi, behavioral characteristics, etc., which can divide users into different subdivisions, so as to achieve personalized identification and service of users. The method of generating a user portrait generally includes the following three steps:
Feature extraction
Feature extraction refers to extracting information from the user's original data that helps describe the user's characteristics, such as the user's age, gender, region, occupation, education level, income level, marital status, hobbies, consumption preferences, behavioral Xi, etc. This information can help us understand the needs and personalities of our users, so that we can provide users with more suitable and satisfactory products and services. For example, we can recommend clothing and cosmetics that are more suitable for users based on their age and gender, so as to increase users' purchase intent and loyalty. We can also provide users with content and services that are more in line with their culture and profession according to their geography and occupation, so as to improve user engagement and satisfaction.
The purpose of feature extraction is to reduce the dimension of the data, reduce the redundancy and noise of the data, and improve the quality and validity of the data. The dimension of data refers to the amount and type of information contained in the data, the redundancy of the data refers to the duplicate or irrelevant information in the data, and the noise of the data refers to the erroneous or abnormal information in the data. Reducing the dimensionality of data can reduce the cost and time of data storage and processing, and improve the readability and comprehensibility of data. For example, we can convert a piece of a user's text data into a vector composed of words or phrases, thereby reducing the size and complexity of the data and improving the expressiveness and operability of the data. Reducing data redundancy and noise can improve the accuracy and consistency of data, and improve the reliability and validity of data. For example, we can remove background and noise from the user's image data, thereby improving the clarity and relevance of the data, and improving the reliability and validity of the data.
There are many methods of feature extraction, such as statistical analysis, cluster analysis, association analysis, factor analysis, principal component analysis, decision trees, neural networks, etc. These methods all use mathematical and statistical principles and techniques to extract meaningful and useful information from data, thereby simplifying and optimizing the structure and presentation of data. Different methods are suitable for different data types and scenarios, with different advantages, disadvantages and effects. For example, statistical analysis methods can extract basic descriptive information from data, such as mean, variance, frequency, distribution, etc., which is suitable for preliminary exploration and analysis of data, but cannot extract the deep characteristics and laws of data. Neural network methods can extract complex nonlinear information from data, such as feature combinations, transformations, relationships, etc., which are suitable for advanced modeling and data modeling, but require a large amount of data and computing resources, and are difficult to interpret and understand.
Model training
Model training refers to the use of extracted feature data to build and train a mathematical model that can classify or improve users, such as linear regression, logistic regression, support vector machine, naïve Bayes, random forest, k-nearest neighbors, neural network, etc. These models use mathematical and statistical principles and techniques to Xi the characteristics and patterns of users from the data, so as to divide or evaluate users. For example, we can use a linear regression model to determine the user's consumption level based on their age, gender, income, and other characteristicsWe can also use the support vector machine model to classify the user's personality type according to the user's interests, consumption preferences, behavioral Xi and other characteristics.
The purpose of model training is to find a model that can maximize the fit and generalization ability of the data, that is, it can achieve high accuracy on the training set, and at the same time maintain good performance on the test set and unknown data. Fit refers to the degree to which the model fits the data, and generalization ability refers to the degree to which the model adapts to unknown data. The higher the fit, the better the model can capture the characteristics and patterns of the dataThe stronger the generalization ability, the better the model can adapt to different data distributions and changes. For example, we can use the method of cross-validation to evaluate the fit and generalization ability of the model, that is, divide the data into a training set and a test set, use the training set to train the model, use the test set to test the model, compare the performance of the model on the two datasets, and select the optimal model.
There are many methods for model training, such as gradient descent, stochastic gradient descent, Newton's method, quasi-Newtonian method, conjugate gradient method, least squares method, maximum likelihood estimation, maximum posterior estimation, cross-validation, regularization, ensemble Xi, etc. These methods use mathematical and statistical principles and techniques to find the optimal model parameters from the data, so as to optimize and improve the performance and efficiency of the model. Different methods are suitable for different models and data, with different advantages, disadvantages and effects. For example, the gradient descent method is an iterative optimization algorithm that minimizes the loss function of the model by continuously updating the model parameters in the opposite direction of the gradient, which is suitable for most models, but requires an appropriate Xi learning rate and number of iterations, otherwise it may cause the model to converge slowly or fall into a local optimum. Least Squares is an analytic optimization algorithm, which minimizes the loss function of the model by solving the normal equation of the model parameters, which is suitable for linear models, but needs to calculate the inverse matrix of the data, and when the dimension of the data is high, it may lead to a large amount of computation or singular matrix.
Profile generation
User portrait generation refers to the use of a trained model to classify or classify the user's characteristic data, so as to obtain the user's label or score, such as the user's personality type, consumption level, purchase intention, churn risk, loyalty, satisfaction, etc. These tags or scores can help us understand the characteristics and needs of users more intuitively and concretely, so as to provide basis and guidance for product design and operation decisions. For example, we can provide users with product features and interface styles that are more suitable for them according to their personality type, so as to improve the user's experience and satisfaction. We can also provide users with more reasonable and preferential products and activities according to the user's consumption level and purchase intention, so as to improve the user's purchase rate and repurchase rate.
There are many ways to generate user portraits, such as threshold division, scoring rules, rating systems, and tag systems. These methods use mathematical and statistical principles and techniques to transform user characteristic data into information that is easier to understand and operate, thereby simplifying and optimizing the structure and expression of user portraits. For example, we can use the method of threshold division to divide users into three levels: high, medium and low according to their consumption level, so as to provide different products and services for different levels of users. We can also use the method of scoring rules to give users a score of 0 to 10 according to their purchase intentions, so as to provide different strategies for users with different scores.
The role of large models in generating user portraits
Large models refer to deep academic Xi models with ultra-large-scale parameters and data, which can exhibit strong generalization ability and creativity in multiple domains and tasks, such as natural language processing, computer vision, speech recognition, recommender systems, etc. The role of large models in generating user portraits is mainly reflected in the following three aspects:
The role of large models in feature extraction
The role of large models in feature extraction is to improve the quality and diversity of data. What is a large model?Large models refer to Xi models with ultra-large-scale parameters and data, which can exhibit strong generalization ability and creativity across multiple domains and tasks. Deep Xi model is a machine Xi method that uses artificial neural networks to learn Xi data features and rules, which can simulate human cognitive and thinking processes. Parameters are variables in a deep Xi model that can adjust the behavior and output of the model. Data is the input and output of deep learning Xi models, and they can reflect real-world information and knowledge. Hyperscale parameters and data mean that the model can process more information and knowledge, making the model more intelligent and flexible.
Large models can extract richer and deeper features from the user's raw data, such as the user's emotions, attitudes, intentions, preferences, values, etc. These characteristics can not only better reflect the real needs and personality of users, but also increase the dimension and complexity of the data, thereby improving the amount of information and discrimination of the data. Dimensions are attributes of the data, and they can describe the characteristics and characteristics of the data. Complexity is the difficulty of the data, and they measure the diversity and chaos of the data. The amount of information is the value of data, and they can reflect the usefulness and importance of data. Differentiation is the difference in data, and they can reflect the uniqueness and personalization of the data. For example, a large model can extract the user's semantics, pragmatics, tone, style, emotion, expression, voice, action and other hierarchical features from the user's text, image, audio, ** and other forms of data, so as to build a more comprehensive and detailed user portrait. User persona is a method of analyzing and describing user characteristics and behaviors, which can help product managers and operations staff better understand the needs and preferences of users, so as to provide better and personalized products and services.
In order to give you a clearer understanding of the role of large models in feature extraction, I have prepared some examples and diagrams for you. Take a look at the following:
Example 1: The large model can extract the user's emotional characteristics from the user's text data, such as joy, anger, sadness, satisfaction, dissatisfaction, and dislike. These emotional traits can help product managers and operations staff understand the psychological state and satisfaction of users, which can lead to increased user loyalty and retention. For example, a large model can extract the user's emotional tendencies, such as positive, negative, or neutral, from the user's comments on a movie, as well as the user's emotional intensity, such as strong, average, or weak. In this way, product managers and operations staff can optimize the recommendation and evaluation system of movies based on users' emotional feedback, thereby improving users' viewing experience and word-of-mouth. Here's a diagram that shows how the large model extracts the user's sentiment features from the user's text data:
Example 2: The large model can extract the user's style characteristics from the user's image data, such as simplicity, retro, fashion, personality, etc. These style traits can help product managers and operations staff understand the user's aesthetics and preferences, so as to provide more suitable and personalized products and services. For example, a large model can extract the user's style preferences, such as color, shape, texture, accessories, etc., from the ** photos uploaded by the user. In this way, product managers and operations staff can recommend more suitable products such as beauty, clothing, and accessories based on the user's style preferences, thereby improving the user's shopping experience and satisfaction. Below is a schematic diagram that shows how the large model extracts the user's style characteristics from the user's image data:
Example 3: The large model can extract the user's voice characteristics from the user's audio data, such as pitch, volume, timbre, and speech rate. These voice signatures can help product managers and operations staff understand the emotions and personalities of users, so as to provide more humane and intimate products and services. For example, a large model can extract the user's voice characteristics from the user's voice commands, such as happy, angry, nervous, relaxed, etc. In this way, product managers and operations personnel can adjust the voice response of the intelligent assistant according to the user's voice characteristics, such as tone, intonation, and speech speed, so as to improve the user's interactive experience and trust. Below is a diagram that shows how the large model extracts the user's voice characteristics from the user's audio data:
The role of large models in model training
The role of large models in model training is to improve the performance and efficiency of the model. Model training is to let the computer learn and optimize its ability to Xi and optimize itself through data and algorithms, so that it can complete different tasks, such as identifying objects in **, or answering user questions. Large models can make model training more efficient and effective, which is reflected in the following aspects:
Improve the accuracy and robustness of the model. Accuracy refers to the correct classification or proportion of data by the model, such as whether the model can correctly identify cats and dogs. Robustness refers to the model's ability to resist noise and anomalies in the data, such as whether the model can still recognize the correct object even if it is blurry or occluded. Large models can learn more knowledge and rules from Xi large amount of data, so as to improve the accuracy and robustness of the model. For example, Wenxin model 40 can learn Xi from massive text, images, speech and ** data, so as to achieve the best results in many fields and tasks, such as natural language understanding, machine translation, image generation, etc.
Increase the versatility and flexibility of your model. Versatility refers to the applicability of a model to different datasets, such as whether the model can work in different languages, domains, and scenarios. Flexibility refers to how adaptable a model can be used in different scenarios and goals, such as whether the model can tailor and optimize its performance according to the user's needs and preferences. Large models can be quickly transferred and Xi and fine-tuned on different domains and tasks, thereby improving the versatility and flexibility of the model. Migration Xi allows the model to adapt to new data and tasks using the knowledge and capabilities it has already learned and Xi. Fine-tuning is to adjust the parameters of the model to make it more suitable for a specific task through a small amount of data and training on the basis of the model. For example, Alibaba Cloud's "Tongyi" large model series can support more than 200 service scenarios through migration Xi and fine-tuning, including speech recognition, speech synthesis, text generation, text summarization, text classification, image recognition, image search, image generation, analysis, and generation, to meet the needs of different industries and fields.
To better understand the role of large models, let me give you an example. Let's say you're a product manager on an e-commerce platform, and you want to improve the shopping experience of your users by allowing them to search and purchase items through voice and **. You can do this with a large model by following the steps:
First of all, you can choose a large model that suits your scene and goals, such as Wenxin Model 40 in vimer-ums, which is a large task model specifically for commodity search, which can extract the features of commodities from images and texts, so as to achieve efficient retrieval.
Second, you can adapt the large model to your data and tasks by transferring Xi and fine-tuning. You can use the existing product** and text data on your e-commerce platform to train a large model and let it learn the characteristics and categories of your products Xi. You can also adjust the parameters of the large model according to your users' preferences and feedback to better match your business needs and scenarios.
Finally, you can deploy a large model to provide users with voice and search capabilities. You can use the API of the large model to allow users to search and purchase goods through voice or ** input. Based on the user's input, the large model will match the most relevant products from your product library and return them to the user. You can also use the multimodal capabilities of large models to provide users with richer information and services, such as generating product descriptions, evaluations, and recommendations based on user input.
Below is a schematic diagram showing the application of the large model on the e-commerce platform:
The role of large models in user portrait generation
User portrait is a model used to describe the basic attributes, characteristics, needs, preferences and other information of users, which can help product managers and operators better understand and serve users, and improve the design and operation effect of products. The construction of user personas usually requires the collection and analysis of large amounts of user data, which is a complex and time-consuming process, and it is difficult to ensure the accuracy and usefulness of user personas. In order to solve this problem, large models play an important role in the generation of user portraits.
Large models are deep Xi models with ultra-large-scale parameters and data, and they can exhibit strong generation and optimization capabilities, as well as strong creative and scaling capabilities across multiple domains and tasks. The large model can use massive user data to generate labels or scores that are more in line with the actual situation and needs of users through automation, so as to improve the fit and credibility of user portraits. Fit refers to the consistency of the user portrait with the user, and credibility refers to the reliability and validity of the user portrait. For example, a large model can generate tags that are closer to the user's interests and preferences based on the user's behavioral data such as searches, clicks, purchases, and evaluations, such as "like sci-fi**", "hobby photography", "pay attention to environmental protection", etc., these tags can reflect the user's real needs and personality, rather than simple demographic attributes, such as "male", "25 years old", "Beijing", etc.
At the same time, the large model can generate more valuable and meaningful tags or scores according to the goals and scenarios of the user portrait, so as to improve the usability and effectiveness of the user portrait. Usability refers to the applicability of user personas in product design and operational decisions, and effectiveness refers to the influence of user personas in product design and operational decisions. For example, a large model can generate professional and detailed labels or scores that are more in line with the application field of user portraits, such as shopping preferences in the e-commerce field, advertising acceptance in the advertising and marketing field, and user retention rate in the user growth field. These tags or scores can help product managers and operations staff provide more personalized and accurate product features and service content for different user groups, and improve user satisfaction and loyalty.
To illustrate the role of large models in user portrait generation, we can take a simple example. Suppose we want to generate user portraits for users of a ** education platform, we can use a large model to generate the following user portraits based on user registration information, academic Xi records, evaluation results, feedback and other data:
From this user profile, we can see that the basic attributes of this user are "female", "35 years old", "Shanghai", and "accountant", which can help us understand the user's background and identity. The user's characteristic tags are "I like English", "I want to improve my workplace skills", "I have a habit XiXi of self-study", "I focus on practical application", etc., which can help us understand the user's interests and needs. The user's score is "learning Xi progress", "learning Xi effect", "learning Xi satisfaction", "learning Xi loyalty", etc., which can help us understand the user's learning Xi performance and attitude. These labels and scores are generated by large models based on the user's data, and they can reflect the real situation and needs of the user, rather than simply categorizing or dividing.
With this user portrait, we can provide users with more suitable product design and operation services according to their characteristics and goals. For example, we can recommend courses and teaching materials that are more suitable for the user's level and needs according to the user's Xi learning progress and effect, or provide users with more practical opportunities and case studies to improve the user's interest and effect in learning Xi. We can also provide users with more discounts and rewards according to their Xi satisfaction and loyalty, or invite users to participate in more interactive and community activities to improve users' Xi satisfaction and loyalty.
Through this example, we can see that the role of large models in user portrait generation is to improve the accuracy and practicability of user portraits, so as to help product managers and operators better understand and serve users, and improve product design and operation results.
How to manage user portraits of large models
User portrait management refers to the process of updating, maintaining and applying user portraits, which can ensure the timeliness, stability and effectiveness of user portraits. There are many methods of user portrait management, such as data cleaning, data fusion, data analysis, data visualization, and data feedback. The role of large models in user portrait management is mainly reflected in the following two aspects:
The role of large models in user persona updates
The role of large models in user portrait update is to improve the timeliness and stability of user portraits. Since user characteristics and needs change over time and in the environment, user personas also need to be constantly updated to keep in sync and consistency with users. The large model can continuously collect and process the latest data of users to achieve real-time updates of user portraits, thereby improving the timeliness of user portraits. At the same time, the large model can analyze and compare the historical data of user portraits to realize the dynamic update of user portraits, so as to improve the stability of user portraits. For example, a large model can update the user's hobbies, consumption preferences, behavioral characteristics, and other tags or scores based on the user's recent browsing, search, click, purchase, and other behavioral data, so as to improve the timeliness of user portraits. At the same time, a large model can analyze the trend and law of user behavior according to the user's long-term behavior data, so as to improve the stability of the user portrait.
The role of large models in user portrait applications
The role of large models in the application of user portraits is to improve the effectiveness and value of user portraits. The application of user portrait refers to the application of user portrait to product design and operation decision-making to improve product quality and user satisfaction. The large model can realize the intelligent application of user portraits by analyzing and optimizing the data of user portraits, so as to improve the effectiveness of user portraits. At the same time, the large model can realize the innovative application of user portraits by creating and expanding the data of user portraits, so as to improve the value of user portraits. For example, a large model can provide users with more personalized and high-quality product recommendations, advertising, content distribution, social interaction and other services based on the data of user portraits, so as to improve the effectiveness of user portraits. At the same time, a large model can provide users with more interesting and useful product functions, activity design, user feedback, user education and other services according to the data of user portraits, so as to improve the value of user portraits.
Summary
In this article, we introduce the generation method of user portraits, as well as the role and advantages of large models in generating user portraits. We hope that through this article, we can help product managers and operators in corresponding industries and fields understand and master the principles and applications of large models, and improve the effectiveness and competitiveness of digital marketing. If you are interested in the content of this article, or want to learn more about the application of large models in digital marketing, I highly recommend you to follow the column "Using AI to Drive Digital Marketing Performance Growth" in my personal account "Product Manager Dugu Shrimp" (the same number on the whole network). This column aims to provide you with more knowledge and skills, so that you can win more users and revenue with large models in the field of digital marketing. The content of the column includes:
The concept and characteristics of the large model.
The development and trend of large models.
Principles and methods of large models.
Applications and cases of large models.
The challenges and opportunities of large models.
Practices and recommendations for large models.
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