In an era dominated by data, businesses struggle with processing diverse, unstructured information across systems. This research presents an AI-powered pipeline addressing product matching challenges in retail and e-commerce. Our solution combines traditional matching algorithms with deep learning through a five-step process. This approach minimizes manual intervention while improving accuracy and efficiency.
Basic Computer Science knowledge.
In today's data-driven world, businesses face the challenge of managing vast amounts of information, often from diverse and unstructured sources. This complexity necessitates efficient and accurate data processing techniques to extract meaningful insights and drive informed decision-making. One such critical task is product matching, where the goal is to accurately identify and link records representing the same product across different systems or datasets. This is particularly crucial for businesses operating in industries with complex product catalogs, such as retail, manufacturing, and e-commerce. Our project solves this problem by developing a robust, AI-powered pipeline to automate the matching process while minimizing human intervention. The task involved matching incoming product records with a standardized catalog, referred to as the "G_List," which includes essential attributes. The primary challenge lay in the inherent variability and inconsistency of product data. Incoming records often included: spelling and grammatical errors, data inconsistencies, multilingual variations. Examples of the challenge include identifying that "Yellow Glass whiskey 1L," "Color Whiskey 1L glas," and "GLAS amarillo whiskey" all correspond to "GLASS YELLOW WHISKEY 1L" in the G_List. Our solution comprises five key steps. First, we created the G_List by standardizing product data and attributes. Next, we consolidated source data into a unified data lake, where records were cleaned and transformed. The third step utilized the tfidf_matcher Python library for initial matching. Step four, feature extraction. Finally, a deep learning model assessed the matches as either "PASS" or "NOT PASS." Records flagged as "PASS" were automatically labeled and finalized, while "NOT PASS" records were escalated for manual review. This hybrid approach of combining matching algorithms with AI significantly reduced manual workload while improving accuracy.
Data Engineer/Scientist at Accenture Baltics. I have been involved in various interesting projects, such as: