In today's rapidly evolving digital landscape, we expect search results to appear fast and tailored to our preferences. However, as the demand for more relevant answers and different search experiences rises, companies must implement a high degree of natural language to understand online users better and deliver accurate content tailored to their requirements.
Enter vector search: a new technology that enables websites and various programs to understand their online users contextually, providing better search experiences and other crucial benefits.
In this guide, we'll explore the many incredible aspects of vector search. Keep reading to know how it works and why it's essential, with real-life examples from known brands worldwide.
Key takeaways
- Vector search uses machine learning applications and certain crucial components to understand context and semantic relationships through number representations.
- This technology is prominent in image and video searches, online recommendations, content filtering, and more.
- Some known brands that utilize this technology include Google, Amazon, Pinterest, and Spotify.
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Understanding Vector Search
Generally, vector search leverages machine learning (ML) techniques and models to capture and understand the meaning of unstructured data, like text and images, by transforming them into numeric representations. In doing so, programs can learn objects contextually and deliver answers based on semantic relationships.
One of the limitations of traditional search is keyword-based matching. Online users often struggle to find information and materials without the proper text combinations. Traditional or keyword search is also sensitive to typos and misspellings. It also has difficulty dealing with ambiguous or vague queries and understanding synonyms and variations.
Meanwhile, vector search utilizes semantic-based matching to understand what online users actually mean, even without getting the right keywords. It can also capture synonyms, related terms, and semantic contexts. Because of that, this technology enables websites and apps to expand their natural language and learn the complexity of different search queries. That means developers can enhance their online user's search experience and provide relevant answers, recommendations, and similar results.
Explaining Vector Search to a Non-Developer
For example, when we think of 'Italian food,' the first thing that comes to mind are pizza or pasta. We learn this from visiting the country or eating at an Italian restaurant. However, computers don’t inherently grasp these associations. The machine might not understand that the foods mentioned are considered the best 'Italian food.' If you ask it to look for an 'Italian restaurant,' the search results could leave out specific pizza or pasta places.
With the combined technology of machine learning and vector search, computers can understand the meaning behind our search. They can look for and provide information based on context from numbers, not exact words or search terms.
Three Components of Vector Search
Vector search involves three critical components for the process to work: vector embedding, similarity score, and nearest neighbor algorithms. Here’s a breakdown of these three components:
Vector embedding
Vector embedding is the process of converting online materials into numbers that represent their meaning. These numbers are stored in a multidimensional space or database, where similar data points cluster together. The closer they are, the higher their relationships with one another.
Developers can transform several types of objects into vector representations. These include words, sentences, images, documents, users, and products. Today, developers utilize different techniques to convert objects into a vector. The most popular ones they use to form sentence-level representations are Word2Vec, GloVe, and FastText.
Meanwhile, others use pre-installed language machine learning models, like BERT or GPT, to provide contextualized embeddings for complete sentences. Generally, these open-source models are based on deep learning architectures like Transformer. These networks help computers understand the contextual relationships between objects, like words and images, more accurately.
Moreover, the vector embedding is typically represented as a sequence of numbers. The length of these numbers will depend on the specific embedding technique used and how the developers want the data to be represented.
For instance, word embeddings often have dimensions ranging from a few hundred to a few thousand, while sentence or document embeddings may have higher numbers than that because they capture more complex semantic data.
Similarity scoring
Vector search engines utilize similarity scoring to quantify the similarities between two vectors. Computing these similarities involves calculating the distances between the numbers in the database. The closer the numbers in the vectors are, the higher their scores will be, meaning these data are similar in terms of context and other factors.
The most common distance metrics used in this process include the Euclidean distance, Manhattan distance, and cosine similarity. The Euclidean distance determines the straight-line gap between two points in a given space, while the Manhattan distance calculates the total of the absolute differences between corresponding dimensions.
Meanwhile, the cosine similarity calculates the cosine of the angle between two vectors. Choosing the distance metric will depend on the data's characteristics and the application developers use.
Nearest neighbor algorithms
The approximate nearest neighbor (ANN) technique helps find similar vectors to a given query. Experts call it approximate because vectors are often embedded in high-dimensional space, meaning these algorithms trade off small amounts of accuracy for a significant boost in search speed.
Today, several ANN algorithms are available to handle large-scale similarity searches. Some of the common ones are the k-Nearest Neighbors (kNN), Space Partition Tree and Graph (SPTAG), Hierarchical Navigable Small World (HNSW), and Facebook's similarity search algorithm (Faiss).
Why Brands Implement Vector Search
Vector search enables computers to understand what online users mean. However, on the surface, vector search benefits companies in different ways. Here are some of the common advantages of enabling vector search.
Offer relevance for better engagement
Through vector search, websites and apps can provide more helpful results, valuable recommendations, and relevant materials to online users. Because of that, companies can offer meaningful experiences to prolong and enhance engagement on any digital platform, boosting traffic and, potentially, revenue.
Providing fast search results
As mentioned, nearest-neighbor algorithms prioritize search speed on large-scale and high-dimensional spaces. Because of this technology, programs can provide fast and contextually relevant results to an online user even when the search becomes resource-intensive.
Achieving extensive search class
Vector search engines allow brands to offer new types of results for online users. That means aside from words and sentences, websites and apps can upload images, videos, and documents to provide more specific and relevant materials to the target audience. This is prominent in e-commerce websites and social media platforms, which we'll explore below.
Use Cases for Vector Similarity Search
The benefits mentioned above are general in all industries that use vector search in their programs. To further explore these advantages, we're providing some of this technology's most common use cases. We're also providing some known brands utilizing vector search services.
Image and video search
Materials like images and videos are high-dimensional and resource-intensive. Through vector databases, similarity search within these visual data is possible and with a significantly faster return time. With vector search technology, companies that require vast visual data can conduct tasks like image categorization, content-based recommendation, duplicate detection, and video retrieval.
Take Pinterest as an example. When users pin images on the platform, a vector database will convert those pinned images as high-dimensional embeddings. Because of that, Pinterest can recommend similar photos based on these pins, enhancing a user's content discovery and engagement.
Meanwhile, eBay is an excellent example of an e-commerce site utilizing vector databases to enhance image search. The platform provides recommendations based on user history and behavior, particularly images that allow shoppers to discover similar items that meet the style they prefer.
Recommendation and personalization settings
Generally, recommendation systems rely on similarities between user preferences and items on a database. With a vector search, platforms that offer this functionality can provide faster and real-time suggestions to online users.
The most famous example of this function is from streaming platforms like Netflix. A vector database can represent factors like genres, actors, and user reviews, offering a more tailored viewing experience based on a user’s watch history. On the Netflix platform, these recommendations often appear in the 'Top Picks for [USERNAME]' section.
The music streaming platform Spotify also heavily relies on vector search to heighten its recommendation settings and user listening experiences. Songs are represented as vectors based on artist, genre, rhythm, melody, and instrumentals. We often see these suggestions in the platform's 'Discover Weekly section or when the app recommends one of their curated playlists. They also use this technology for suggesting podcast channels based on genre and specific topics.
On the other hand, e-commerce platforms leverage vector search to provide a more personalized shopping experience. They do so by embedding products based on specific attributes, including color, style, fabric, and customer reviews. That way, when a user browses for an item, the site can provide products with suggestions relevant to their preferences and past purchases.
Social network analysis
Vector search is also primarily used in social networks to find similar individuals based on connections, past interactions, and behaviors. Embedding people as vectors will allow systems to measure similarities between them and others in the database, enabling programs to find communities, suggest potential connections, and find influential accounts.
Social media apps like Instagram and TikTok use this feature to recommend content and pages to users. It’s also vital for brands to identify and connect with relevant accounts that can help elevate their online presence. It’s crucial for them to acquire honest feedback from user-generated content to refine their strategies.
Natural language processing (NLP)
As discussed above, vector search enables computers to improve their natural language processing, understanding their users better contextually. One of the best examples of this is how online users interact with chatbot systems. Customer queries can be converted into vectors, which allows such tools to understand and respond to query variations more accurately.
Content filtering and search
Through vector search, online users can have more meaningful and insightful interactions with various sites. Embedding documents, articles, and online pages as vectors will help companies provide information that actually resonates with their visitors. This content filtering feature is prominent in news and blog websites and search engine platforms.
Anomaly detection
Aside from improving user experiences online, vector search helps detect anomalies in certain areas. Detection programs use vectors to compare data points to a recurring or expected distribution or process. Any deviations from these data sets will be considered anomalies.
This feature is helpful in biometrics systems. For instance, facial recognition in international airports captures a person's face and converts it into a vector. These systems will check their faces and match them against a vector database of persons of interest or criminals.
Pro Tip
Leverage vector search to offer personalized experiences to online users. With similarity search, you can tailor product recommendations, content, and marketing campaigns based on your target audience's behavior and preferences.
Maximize Every Tool Available
With vector search technology, websites and apps can provide online users with meaningful interactions and relevant information. Incorporating them and other modern tools and techniques on your digital channels will help boost engagements significantly and improve certain online services.
Vector search on social media has allowed platforms like Instagram and TikTok to recommend relevant accounts and their user-generated content. These content forms enable brands to showcase their credibility by leveraging other people’s honest reviews.
Unfortunately, some user-generated content, like stories, is time-sensitive. Missing out on these could make you miss out on various opportunities to boost your online credibility. With smart tools like Archive, you won't have to deal with this issue. Visit Archive today to learn more.
Take advantage of every tool available. Check out Archive and learn how to maximize different social media solutions for your campaigns.
FAQ on the Advancements of Vector Search
What are some of the best vector search databases available?
Vector databases are designed to organize data based on similarities. The best options developers use today include Chroma, Pinecone, Weaviate, and Milvus.
What are some factors to consider when choosing a vector search database?
Choosing the perfect vector database is critical. If you’re looking for the right one for your website or app, some of the main factors to consider are scalability, performance, flexibility, and ease of use. However, the choice must always depend on your specific needs.
Can you use a vector database without developers?
No. Using it requires technical expertise. While some platforms now exist to make vector databases accessible to non-developers, seeking the help of professionals is still vital to navigating the space more easily and achieving vector search more effectively.
