How Ecommerce Businesses Can Take Advantage of Data Labeling
Accurate search results and personalized recommendations are undisputedly the bedrock of modern ecommerce . As more businesses around the globe migrate online, the goal of every such company is simple — to help the user find what they’re looking for quickly and easily in order to enable maximum spending on the e-platform.
To make all of this possible — from search query relevance to ranking models and recommendation engines — machine learning (ML) is utilized. Three key aspects are required to see any ML system succeed: algorithms, hardware and the actual data. While the first two are readily available and pose no serious obstacles, data collection and labeling remain a major hurdle. Data Labeling
How do we find and label data in a way that’s fast, accurate, affordable, and sustainable? Not only do businesses want to pay and wait less, but they also want reliable, high-quality data. And not on a one-off basis, but consistently — datasets and ML algorithms need to be updated and improved post-deployment .
A variety of options exist. One of the approaches is known as human-in-the-loop data labeling and falls under the general human-centric AI paradigm. This method makes use of human labelers and uses aggregation techniques, thereby producing large datasets resistant to the mistakes of individual performers. Industry Trends and In-Demand Tasks
Among the many ecommerce tasks offered by human-in-the-loop third parties are: Recommender systems (RS)
Search results relevance
Online catalog improvement
Price optimization Quality assurance and customer support Design, PR, and marketing Let’s briefly look at three use cases based on this year’s trends: Product search relevance. This very common and important task is about making sure that when the user types a specific brand and/or model of a gadget in the e-platform’s search bar — be it a phone, tablet, […]
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