What Is Vector Similarity Search?

Regardless of your industry, you probably know that staying abreast of new technologies is an important part of keeping a company competitive and able to meet emerging needs. Vector similarity search is a relatively new option that could soon become more relevant for businesses. Here’s what you need to know about it.

What Is Vector Similarity Search?

As you might guess from its name, a vector search involves representing pictures or bits of text as vectors, or embeddings. They often help train machine learning models. Object similarity also comes into play, which compares the vectors in a multidimensional space.

A vector search involves representing pictures or bits of text as vectors, or embeddings.

Closeness represents more vector similarity between the embeddings, whereas more distance means fewer common characteristics. People using a vector search engine could perform nearest-neighbor searches to determine the closest query-related vectors in a space.

How Could Businesses Use Vector Similarity Search?

A vector search uses deep learning and other advanced techniques to answer queries based on contextual understanding rather than a simplistic assessment. Here are some specific ways your organization could see the advantages of vector similarity searches.

Show Users Similar Products

Giving e-commerce site visitors similar or exact matches to the things they search for is crucial for prolonging their engagement and increasing the likelihood of a sale. The results of a vector search could be useful for showing shoppers such content.

eBay developed an interactive visual search feature based on the vector similarity approach. Users choose pictures from batches of images that most closely match the products they want. People see new groups after making their selections.

The idea is to gradually narrow down the online marketplace’s massive product assortment while encouraging item discovery.

Develop Deep Learning Algorithms More Quickly

Vector similarity search relies on deep learning — an advanced form of artificial intelligence (AI) based on how human brains process information. There’s a growing push for businesses of all types to see how AI could help them.

For example, statistics show it’s possible to get up to 20% conversion cost reductions when applying AI to core business practices in producing industries, according to BlueSentry. However, many companies don’t have people on their teams with extensive AI knowledge, and it’s not always easy to find such specialists quickly.

That said, a company called SentiSight.ai offers an online dashboard geared toward people who want to develop similarity search models, but don’t have extensive prior knowledge.

For example, the product’s image similarity search function can make predictions about pictures a person uploads from their mobile phone. There’s also an AI-assisted labeling feature.

Read more: AI Software Trends for 2021

Improve Security Measures

Maintaining a high level of physical and cybersecurity is vital for today’s businesses to succeed. For example, vandalism or product theft could cut into profits, while a ransomware attack could lock company representatives out of critical systems and data.

A recent study found that one in three organizations are experiencing more cybersecurity attacks this year than last. Fortunately, vector similarity approaches could help in both regards.

A vector search engine could store data about people who have stolen from a store previously, allowing an algorithm to recognize possible problematic shoppers. Computer codes could also feature in vector similarity searches. According to GSI Technology, checking a piece of software against a database of known vulnerabilities could tell a company whether the product has issues for hackers to exploit.

Read more: What Is Adversarial Machine Learning?

Improve SEO Strategies

You may also use a vector search within a search engine optimization (SEO) strategy. That’s because it can help you find word synonyms that could help you include phrases relevant to customers’ primary search terms.

For example, Word2Vec finds the words most similar to an originally inputted one. It works with pieces of text and assigns one vector to each word. The closest vectors represent the best-matching terms.

Read more: AI vs. Machine Learning: Their Differences and Impacts

What Could Negatively Affect Widespread Adoption?

Now that you know why company representatives might use a vector search engine strategy in their operations, you might wonder why this technology is not yet part of the mainstream. The main challenge is that vector similarity search is still emerging.

It may not be easy to convince decision-makers to invest in a technology that does not have widespread usage.

Thus, it may not be easy to convince decision-makers to invest in a technology that does not yet have widespread usage. Business leaders often want to see case studies before committing to investments, and it’ll take a while to gather the evidence for those.

Further, vector similarity databases once required significant resources to implement and maintain. However, service providers are making it easier to work with these searches at modest costs. For example, Pinecone offers a fully managed vector similarity search database and a pay-for-what-you-use model.

How Can You Explore Vector Similarity Search Applications?

If you’ve decided to dive into vector similarity search and determine some of the specific ways it could help your business, there are a few options beyond those already mentioned.

Facebook’s engineers unveiled Faiss, a vector similarity search tool, in 2017. Developers reported an 8.5x improvement in processing time when using it across GPUs for nearest-neighbor searches. You can get it on GitHub. However, it’s intended for people with prior coding experience.

There’s also Weaviate, an open-source tool that allows working with any kind of media — including video, audio and text. Milvus is an open-source vector search engine suited to unstructured data, and counts Trend Micro and the Cleveland Museum of Art among its users.

Microsoft offers Bing vector search, another specific tool geared toward developers. And companies in the AWS ecosystem can leverage Amazon SageMaker and Amazon ES to build visual search applications.

Will You Benefit From Vector Search?

This technology is still in the very early stages. As more companies decide to use it, the potential applications will become even more evident and exciting.

If you’re strongly considering using vector search based on what you’ve read here and elsewhere, start by envisioning a few business cases or goals you want to meet. Then, research more deeply into how vector similarity searches could provide the necessary assistance.

Read next: Top Big Data Tools & Software for 2021

Devin Partida
Devin Partida writes about business technology and innovation. Her work has been featured on Yahoo! Finance, Entrepreneur, Startups Magazine, and other industry publications. She is also the Editor-in-Chief of ReHack.

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