Vector search is using machine learning to translate the similarity of text, images or audio to make a more scalable and accurate search.
It’s a way to find related objects with similar characteristics through advanced AI capabilities. By detecting semantic relationships between objects in an index, vector search can help to produce more accurate results. If you want to deepen your understanding of vector search and its applications, Elastic’s detailed guide offers comprehensive insights into this technology. Learn more about vector search here.
Why We Need Vector Search
The problem with language is that it can sometimes be ambiguous and unclear. Synonyms can be a problem, where two words have the same meaning. Polysems can also occur, where the same word has multiple meanings. This can cause difficulties for a search function. Vectors and machine learning can make sense of language.
Vectorization
Vectorization is the process of converting words into vectors (numbers). It encodes meanings of words and processes them mathematically, translating them into a mathematical system. This can then be used to detect synonyms, meanings and intent, as well as ranking results. It can also create clusters of words that can be grouped together in a sensical way.
Creating Vector Embeddings
Natural Language Processing (NLP) is the system used to analyze text and infer meaning and structure, vector embeddings is one of these techniques. They use neural networks which are a set of algorithms that are loosely modeled after the neurons of a brain. This deep learning creates mathematical functions trained on huge datasets to recognize large numbers of complex patterns.
Vector Search Results
Nowadays we don’t have a problem processing different data types such as images, videos or audio. But vector results can offer more accurate results for hard to process queries. To further explore the capabilities and advancements in vector search, Elastic’s comprehensive guide provides in-depth insights. They can also be used to measure similarities between different modalities. For example using images and text, vectors are able to help find accurate answers to one from the other. In difficult cases, where keyword search or natural language search can fall short, vector search has the capacity to deliver similar results.
Vector Search Challenges
Vector search needs to be more accurate than keyword search in order for it to be widely employed in a meaningful way. A challenge here is that vectors need to be tailored so they are calibrated for specific stores. Using a general model will not provide such accurate results.
Currently there are limitations with speed and scale, but these are being worked upon. Some businesses are choosing to employ a hybrid approach. This uses both vector search and keyword search, to provide the optimal results.
Where To Use Vector Search
Vector search can be used in a variety of ways across your eCommerce store:
- Semantic search: finding what users mean without requiring an exact keyword match
- Recommendations: determining similar documents and vectors based on past actions
- Question answering: using vectors to understand intention and aim
- Personalization: vectors can be used to describe user behavior
Moving Forward
Vector search is important as it overcomes current limitations facing the eCommerce experience. It will provide quick and accurate results to queries, and meet the customers at a more human level. Improving these functionalities on your site will improve user experience, and boost brand image and loyalty.
