Key Terms To Better Understand Vector Search In Ecommerce

This post will help you better understand Vector Search in eCommerce. Vector Search transcends current eCommerce limitations, providing quick and accurate results to queries. Improving these functionalities on your site improves user experience and boosts your brand image. The more you know about Vector Search, the better quality experience you can provide to your shoppers. Let’s take a look at some key terms now.

Vector Search

Vector search is the use of machine learning to translate the similarity of text, images or audio in order to make a more scalable and accurate site search. It’s a method of finding related objects with similar characteristics through advanced AI capabilities. Through detecting semantic relationships between objects in an index, vector search can help to produce more accurate results.

Semantic Search

Semantic search is a data search technique that uses the intent and contextual meaning behind any given search query. This can help deliver more relevant and accurate results. Understanding the semantics means understanding your customer’s intent, made possible through a combination of NLP (Natural Language Processing) and deep learning. By understanding what the searcher is asking and the user intent behind the keywords, the results will be relevant. It doesn’t rely entirely by matching keywords, but by going deeper.

Embeddings

Embeddings are ways of representing complex information (text, images) using a set of numbers. High-dimensional vectors that are typically used for keyword-based search are translated into a lower-dimensional space, which makes it easier to work with large inputs. Through capturing the semantic process of the input and placing similar inputs close together, easy comparisons and analysis can be made of the information. This is represented in the vector space by a set of numbers which can be re-used in various applications.

Generative AI

Generative AI uses neural networks to identify patterns and structures from existing data in order to generate new content. This enables users to quickly generate content based on their inputs. These could be:

• Text
• Images
• Sound
• Animations
• 3D models
• And more.

Large Language Models

A large language model is an AI algorithm that uses deep learning techniques and huge data sets to understand, generate, predict and summarize new content. Large language models are closely linked to generative AI, which has specifically been created to generate text-based content. Large language models expand the data used for training and inference, and therefore improve the capacities of AI.

Fine Tuning

Fine tuning is an approach to transfer learning, where pre-trained models are trained on new data. It can be done on an entire neural network, or on a sub-set. It occurs when knowledge that was gained from finding a solution to one situation is also applied to a different yet related situation. It’s a way of transferring knowledge already, without having to start from scratch. Instead it just involves tuning or tweaking models in an efficient way.