E-commerce data annotation


Machine Learning and Artificial intelligence (AI) has drastically changed the world of online shopping. It provides services to customers in many ways from ensuring security to providing assistance and making things in a more proper and easy manner. It helps the Retail/ e-commerce space to provide services to their customers on the next level and create satisfactory online shopping experiences. AI is one of the fastest technological successes due to intelligent solutions that are helping to change the e-commerce industry. AI and Machine Learning are helping to deliver the best and most secure shopping experience with the help of data annotation and labeling services provided by Data Labeling companies that make complicated tasks easier. 

Attribute Mining 

E-commerce is an important domain for Attribute mining including many characteristics of product categorization like color, size, gender, size, etc.  Data mining tools generate new information for decision-makers from very large databases various mechanisms of this generation include abstractions, aggregations, summarizations, and characterizations of data.  Learning Spiral transforms content across platforms, devices, & channels so that it resonates locally & delivers superior customer experience.e ensure that your content is relevant, helpful at every step through Attribute Mining.

No doubt, Product attribute extraction is a significant task in e-commerce. Extracting pairs of attributes and adding product descriptions can be useful for many tasks, such as product matching, product categorization, product recommendation. 

Building intelligent E-commerce systems typically involve a component that can automatically extract product attribute information from a variety of product description pages in different E-commerce Web sites.

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To generate sales it’s very important that customers can easily find their requirements in the online shopping portal Machine learning assist with features like search ranking, which allows sorting search results by their estimated relevance. This estimation can take into account frequencies of specific search terms as well as the particular customer profile (e.g. age, budget taste, preferences, previous product views, habits, or previous search terms). So, these machine learning search algorithms become less about listing all products that match a given sequence of letters, and more about predicting what customers might actually want to see, even when they might not know it yet. Another important feature is query expansion, in which the most likely search term completions are suggested while the customer is still typing and searching for the product.

 Product Attribute Mining is necessary for E-commerce to enhance customer experience, increase profits, Balance Demand, and supply, etc. 

Annotated & Labeled Data + Machine Learning + Attribute mining + AI = Strong system for E-commerce

AI, Data Labeling, and Data annotation are supporting online shopping experiences for both retailers and customers. There are many e-commerce businesses that are already using AI for better user experience and many are in the process to make it happen one of the important and beneficial tasks in the Retail/ Ecommerce space is improving the quality of the search engine using machine learning.