Commodity industrial attribute portrait is an important foundation for digital marketing businesses such as e-commerce, advertising and marketing, and user growth, which can help product managers and operators better understand the characteristics, classification, and value of products, so as to improve product quality, conversion, and retention. However, the profiling of commodity industrial attributes also faces some challenges, such as difficulty in data labeling, model training, and model application. From the perspective of a product manager, this paper introduces the role and value of AI models in the industrial attribute portrait of goods, as well as solutions and suggestions on how to deal with these challenges. This article also introduces a special column for the artificial intelligence model of digital marketing business, "Using AI to drive the growth of digital marketing performance", which is hosted by product manager Dugu Shrimp (the same number on the whole network), aiming to share the latest progress and best practices of artificial intelligence models in digital marketing business.
The importance of commodity industry attribute portrait is self-evident, it is an important foundation of digital marketing business, and it is also one of the core tasks of product managers and operators. However, commodity industrial attribute portrait is not an easy task, and it faces some challenges, such as difficulty in data labeling, model training, and model application. Next, we will introduce each of these challenges, as well as solutions and suggestions on how AI large models can help with the profiling of industrial attributes of goods.
Data annotation is the first and most critical step in the portrait of commodity industrial attributes. The quality and quantity of data annotation directly affect the effect and performance of the model. However, data annotation is also the most time-consuming and laborious step, requiring a lot of human involvement, as well as specialized knowledge and tools. The difficulties of data annotation are mainly as follows:
A lot of manual annotation is required. The industrial attributes of commodities are diverse, and each attribute may have multiple values, and different commodities may have different attributes. For example, clothing products may have attributes such as color, size, style, etc., while electronic products may have attributes such as brand, model, specification, and function. In order to ensure the accuracy and consistency of data labeling, it is necessary to have professional annotators to label each attribute of each commodity, which is a tedious and repetitive work that requires a lot of manpower and time. This is a huge challenge and burden for product managers and operators, as they need to continuously recruit, train, manage, and motivate annotators to ensure the progress and quality of data annotation, while also bearing the cost and risk of data annotation.
The data annotation tool is not perfect. The tool of data annotation is an important support for data annotation, which can improve the efficiency and quality of data annotation, and reduce errors and omissions in data annotation. However, the current data annotation tools are not perfect enough, and there are the following problems: First, the interface of the data annotation tool is not friendly enough, the operation is not convenient enough, and it cannot meet different data annotation needs and scenariosSecond, the function of the data annotation tool is not powerful enough to provide sufficient data annotation assistance and intelligence, such as automated, semi-automated, verification, review, feedback, etcThird, the compatibility of data annotation tools is not good enough, and it cannot adapt to different data formats, types and scales, such as text, audio, etc. This is also a huge challenge and burden for product managers and operators, as they need to continuously select, test, evaluate, and optimize data annotation tools to ensure the efficiency and quality of data annotation, while also incurring the cost and risk of data annotation tools.
So, how can the AI model optimize data annotation?AI large models refer to those AI models with ultra-large-scale parameters, data, and computing capabilities, such as GPT-3, BERT, and DALL-E. By pre-training on massive data, these models can learn a wealth Xi of knowledge and capabilities, so that they can be fine-tuned and applied in different tasks and fields, and realize multi-modal, multi-domain, and multi-task artificial intelligence. The data annotation optimization of artificial intelligence large model in the industrial attribute portrait of commodities mainly has the following aspects:
AI large models can provide automated and semi-automated data annotation. The artificial intelligence model can use its powerful knowledge and ability to analyze and understand the data of commodities, such as **, text, and **, so as to automatically or semi-automatically generate industrial attribute labels of commodities and reduce the workload and time of manual annotation. For example, the artificial intelligence model can automatically identify the color, shape, material and other attributes of the product according to the ** of the product, or automatically extract the brand, model, specification and other attributes of the product according to the text description of the product. Of course, the data annotation results of the AI large model also need to be manually checked and reviewed to ensure the accuracy and consistency of the data annotation. This is a huge advantage and convenience for product managers and operators, as they can use the data annotation capabilities of AI large models to greatly improve the efficiency and quality of data annotation, while also saving the cost and risk of data annotation.
Large AI models can improve data annotation tools. The AI model can be used as the core component of the data annotation tool to provide intelligence and assistance for data annotation and improve the efficiency and quality of data annotation. For example, the AI model can provide suggestions and hints for data labeling, such as recommending appropriate attributes and values based on the category and characteristics of the product, or providing attribute reference for similar products based on the similarity of the productsThe AI model can also provide verification and review of data annotation, such as detecting errors and omissions in data annotation, or evaluating and feeding back the results of data annotation. In addition, the AI model can also improve the compatibility and adaptability of data labeling tools, such as supporting different data formats, types and scales, or providing different data labeling interfaces and operations according to different data labeling needs and scenarios. This is also a huge advantage and convenience for product managers and operators, as they can use the data annotation tools of AI large models to greatly improve the efficiency and quality of data annotation, while also saving the cost and risk of data annotation tools.
Model training is the second and most core step in the industrial attribute portrait of commodities. The purpose of model training is to enable the artificial intelligence model to Xi learn the laws and characteristics of the industrial attribute portrait of the commodity according to the annotated commodity data, so as to be able to accurately attribute and classify the unlabeled commodity data. However, model training is also the most complex and energy-intensive step, requiring a lot of data, computation, and optimization. The difficulties of model training are mainly as follows:
The model training data is insufficient. The industrial attributes of commodities are diverse, and each attribute may have multiple values, and different commodities may have different attributes. This leads to the fact that the data of the industrial attribute portrait of commodities is high-dimensional, highly sparsative and highly unbalanced, that is, the attribute dimension of each commodity is very high, but the value of each attribute is very small, and the value distribution of different attributes is very uneven. This brings challenges to model training, because model training requires enough data to cover different attributes and values to avoid overfitting and underfitting of the model, and improve the generalization ability and robustness of the model. This is a huge problem and pain point for product managers and operators, because they need to continuously collect, clean, organize and label product data to ensure the integrity and validity of the data, and at the same time face the update and change of data to ensure the timeliness and dynamics of the data.
The model takes too long to train. The industrial attributes of commodities are diverse, and each attribute may have multiple values, and different commodities may have different attributes. This leads to the high complexity, high number of parameters, and high amount of computation of the model of commodity industry attribute portrait, that is, the model needs to process a lot of input features, a lot of parameters to learn Xi, and a lot of computing resources and time to train and optimize the model. This poses a challenge to model training, because model training needs to be fast and efficient enough to adapt to updates and changes in product data, and improve the real-time and flexibility of the model. This is also a huge problem and pain point for product managers and operators, because they need to constantly select, configure, debug, and optimize the model to ensure the accuracy and stability of the model, and at the same time, they also have to face the update and change of the model to ensure that the model is advanced and innovative.
So, how can large AI models optimize model training?AI large models refer to those AI models with ultra-large-scale parameters, data, and computing capabilities, such as GPT-3, BERT, and DALL-E. By pre-training on massive data, these models can learn a wealth Xi of knowledge and capabilities, so that they can be fine-tuned and applied in different tasks and fields, and realize multi-modal, multi-domain, and multi-task artificial intelligence. The model training optimization of artificial intelligence large model in commodity industrial attribute portrait mainly has the following aspects:
Large AI models can provide data augmentation and data balancing for model training. The AI model can use its powerful knowledge and ability to analyze and generate the data of commodities, thereby increasing the diversity and balance of data and reducing the sparsity and imbalance of data. For example, the artificial intelligence model can generate new commodity data, such as **, text, and ** according to the attributes of the goods, or generate attribute labels of similar products according to the similarity of the goods, so as to expand the scale and coverage of the data and improve the quality and effectiveness of the data. This is a huge advantage and convenience for product managers and operators, as they can take advantage of the data generation capabilities of AI large models to greatly improve the diversity and balance of data, while also saving the cost and risk of data collection and annotation.
Large AI models can provide transfer Xi and multi-task learning Xi for model training. The AI model can use its powerful knowledge and ability to analyze and understand the data of commodities, so as to extract the commonality and characteristics of the data, and realize the cross-domain and cross-task migration and sharing of data. For example, the AI model can select the appropriate pre-trained model according to the category and characteristics of the product, or select the appropriate fine-tuning task according to the attributes and values of the product, so as to reduce the number of parameters and calculations of the model and improve the effect and performance of the model. This is also a huge advantage and convenience for product managers and operators, as they can take advantage of the transfer Xi and multitasking Xi capabilities of large AI models to greatly improve the effectiveness and performance of models, while also saving the cost and risk of model selection and configuration.
The application of the model is the third and final step in the portrait of the industrial attributes of the commodity. The purpose of the model application is to enable the AI model to provide product managers and operators with the results and services of the industrial attribute portrait of the commodity based on the unlabeled commodity data, ** and classify the industrial attributes of the product. However, model application is also the most complex and energy-intensive step, requiring a lot of data, calculations, and optimization. The difficulties in the application of the model are mainly as follows:
It is difficult to apply the artificial intelligence model in the portrait of the industrial attributes of commodities. Although large AI models have strong knowledge and capabilities, they also have some limitations and defects, such as the interpretability, trustworthiness, and controllability of the model. This brings challenges to the application of the model, because the application of the model needs to ensure the correctness and rationality of the model, so as to avoid errors and deviations of the model, and improve the reliability and security of the model. This is a huge problem and pain point for product managers and operators, because they need to constantly monitor, evaluate and optimize the results of the model and classification to ensure that the model meets the needs and goals of the business, and at the same time face the uncertainty and changes of the model to ensure the adaptability and stability of the model.
Solutions and recommendations. In order to solve the application difficulties of artificial intelligence large models in the portrait of commodity industrial attributes, we need to optimize and improve from the following aspects: first, improve the interpretability of the model, that is, let the model be able to explain and explain the results of its classification and classification, such as giving the reasoning process and basis of the model, or giving the confidence and uncertainty of the model;The second is to improve the credibility of the model, that is, to enable the model to verify and evaluate the results of its classification and classification, such as giving the accuracy and recall rate of the model, or giving the error rate and error rate of the modelThe third is to improve the controllability of the model, that is, to allow the model to adjust and optimize the results of its classification and classification, such as giving the parameters and hyperparameters of the model, or giving feedback and suggestions to the model. This is a huge advantage and convenience for product managers and operators, because they can take advantage of the explainability, trustworthiness and controllability of AI large models to greatly improve the correctness and rationality of the model, while also saving the cost and risk of model monitoring and evaluation.
From the perspective of a product manager, this paper introduces the role and value of artificial intelligence large models in the industrial attribute portrait of commodities, as well as solutions and suggestions on how to deal with the difficulties in data labeling, model training and model application. I hope this article can be helpful and enlightening for your digital marketing business, so that you can better use the artificial intelligence model to optimize the industrial attribute portrait of goods, so as to improve the **, conversion and retention of goods.
If you are interested in the application of AI models in digital marketing business, we have another good news for you. There is a column "Using AI to Drive Digital Marketing Performance Growth" in my personal account "Product Manager Dugu Shrimp" (the same number on the whole network), which is dedicated to the application of artificial intelligence models in digital marketing business, sharing the latest progress and best practices, covering e-commerce, advertising and marketing, user growth and other fields and scenarios, such as product recommendation, advertising creativity, user portraits, etc. The columnist, product manager Dugu Shrimp (the same number on the whole network), is a senior product manager with many years of experience in digital marketing, who has participated in and been responsible for a number of well-known e-commerce, advertising and marketing and user growth projects, and has a deep understanding and rich practice of the application of artificial intelligence models in digital marketing business. In this column, I will introduce you in detail the application scenarios, application effects, application difficulties, and application skills of artificial intelligence models in digital marketing business in various forms such as case analysis, prescriptions, and technical principles, so that you can better grasp the principles and methods of artificial intelligence models, and better use artificial intelligence models to improve your digital marketing performance.
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