Leveraging AI and Machine Learning for Product Matching

2Aug - by aiuniverse - 0 - In Reinforcement Learning

Source: thevistek.com

There is a vast number of products sold online through various outlets all over the world. distinguishing, matching and cross-checking product for functions like worth comparison becomes a challenge as there aren’t any international distinctive identifiers. There are several things wherever accurately distinguishing a product match is crucial. as an example, stores might want to check competition costs for the constant product they’ll supply. Similarly, customers could use comparison tools to urge the most effective deals.

To improve the consumer experience, e.g., by allowing for easily comparing offers by different vendors, approaches for product integration on the Web are needed. we present an approach that leverages neural language models and deep learning techniques in combination with standard classification approaches for product matching and categorization. In our approach, we tend to use structured product information as oversight for coaching feature extraction models able to extract attribute-value pairs from matter product descriptions. To minimize the need for lots of data for supervision, we use neural language models to produce word embeddings from large quantities of publicly available product data marked up with Microdata, that boost the performance of the feature extraction model, therefore resulting in higher product matching and categorization performances.

Leveraging multiple data sources

Identifying an identical product is additionally necessary to construct the ultimate item page. product may be represented in terms of their options like the whole, color, size, etc. so as to create it easier for sellers to aboard their things, most product options don’t seem to be obligatory for sellers to supply. As a result, we discover that completely different sellers could give different options in their product feed. By utilizing totally different sources of knowledge for a constant product, we will increase the coverage of product specifications on the item page.

AI Implementations in the E-Commerce Value Chain

Product Searching: Product looking is one in every of the foremost oftentimes used and necessary options for e-commerce platforms. Customers are able to notice product matching their interests through keywords, wherever product matching depends on informatics technologies; and visual “search by image,” that leverages pc vision. E-commerce platforms conjointly utilize reinforcement learning technologies to optimize their ranking algorithms and deliver higher search results.

personalized Product Recommendation: additionally to looking, e-commerce platforms conjointly use machine learning and informatics techniques to have interaction customers and build personalized product recommendations supported their searching trends and browsing history.

Dynamic Pricing: several e-commerce platforms use dynamic valuation tools steam-powered by massive information and machine learning algorithms to create time period worth changes or predict future costs supported to provide and demand projections.

Fraud Risk Management: E-commerce retailers utilize machine learning technologies to spot potential deceitful MasterCard transactions to forestall and manage risks in real-time and guarantee secure online payments.

Limitations of AI Application in E-commerce

Cold start Problem: because of information insufficiency, retailers in operation a replacement business on an e-commerce platform might not be able to benefit of advanced AI-based options like recommendation system and a dynamic valuation that think about massive information and analytics.

Algorithm Scalability Issue: Reinforcement learning technology will encounter performance bottlenecks on e-commerce platforms. Algorithms typically struggle with scaling issues and may face challenges effectively and expeditiously ransacking through terribly giant call areas.

Long Tail Effect: E-commerce recommendation algorithms could gift only a little variety of the foremost common things to customers, and fail to suggest rare “long-tail” product that might be a lot of appealing to niche customers, in and of itself product will as an example lack spare ratings information.

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