Product matching presents numerous challenges for businesses due to the abundance of products, constant updates, diverse descriptions, lack of standard identifiers, and the presence of counterfeits.
To overcome these obstacles, companies require advanced algorithms, machine learning and NLP techniques together with robust data integration processes for accurate and efficient matching.
Precise product matching offers several benefits for businesses, including increased customer satisfaction, reduced returns, improved inventory management, enhanced data analysis, and operational costs reduction.
In the realm of contemporary product matching, a diverse array of ML algorithms and distinctive features are harnessed to gauge the resemblance between products. By leveraging an extensive repertoire of similarity algorithms, we can construct comparison tools that make use of varying levels of product data. The subsequent points delineate the fundamental elements that are commonly employed in product matching today.
Harnessing the cutting-edge prowess of advanced language models such as BERT or GPT-4, it becomes possible to fashion a module specifically designed for assessing the similarity of titles and descriptions. This module acquires the ability to comprehend contextually similar titles and descriptions, even when the titles being compared differ markedly.
For instance, let us consider three titles that pertain to the same product:
Price comparison assumes a pivotal role within more comprehensive product matching algorithms. To facilitate this process, we deploy two primary data analysis algorithms that assist us in establishing price similarity:
Detection of Price Outliers: By identifying outliers, we are able to detect similarities when comparing a single price to a larger cohort of comparably priced items, which would otherwise remain unnoticed.
Clustering: Employing clustering algorithms like K-means, we gain a profound understanding of the market size for similar products based on pricing. The insights derived from clustering can be effectively employed as features within our overarching product matching system.
Image Similarity: Leveraging the immense potential of powerful deep learning algorithms, image similarity plays a critical role in the realm of product matching. This approach enables us to ascertain the similarity between two products, irrespective of factors such as angle, image quality, design size, or background. Width.ai's cutting-edge image module excels at efficiently learning and comparing product images.
In the process of matching products, certain product attributes such as brand, size, condition, model number, and available colors can prove to be highly effective data points. We categorize these attributes into two distinct types:
Limited Range Values: Attributes with a predetermined range of values, such as colors or clothing sizes (small, medium, large), can be transformed into one-hot encoded vectors, thereby rendering them compatible with AI algorithms.
Endless Values: Attributes that lack a definitive range fall into this category. As the range of potential values expands, the accuracy of our models may diminish. Nonetheless, we rely on bespoke neural networks to learn the relationship between product similarity and these attributes.
Incorporating user-generated content (UGC) analysis into our product matching solution allows us to gain valuable insights. We have developed a tailor-made tool based on the formidable GPT-3, which has the capacity to glean key talking points and keywords from product reviews. This information empowers us to comprehend the similarity between products based on how customers perceive them, transcending mere presentation for sale. Adopting this customer-centric approach significantly enhances the efficacy of our product matching process.
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