Vector search is a search method used to find similar items in a dataset based on their vector representations. In the modern search context, vector search allows search engines to understand the underlying semantics of natural language queries and documents, and to retrieve results that are more relevant and accurate.
By representing words and phrases as vectors in a high-dimensional space, vector search can capture the complex relationships between them, including synonyms, polysemy (the same word having different meaning), and contextual meaning.
Let's take a deeper look at vector search, the underlying tech that powers it and if it is the right choice for your site search functionality.
How vector search decodes semantic relatedness
Semantic relatedness refers to the degree to which two pieces of text, such as words, sentences, or documents, are semantically similar or related to each other in meaning.
The vector search model estimates semantic relatedness by using word embeddings (or vector embeddings) which are numerical representations of words that encode the context in which they appear in a piece of text. Let's understand the workings of vector search with an example.
When a user types in the query 'black leather boots' on a vector search-driven ecommerce store, the store's search engine wouldn't just match the words "black", "leather", and "boots" to the product descriptions (which is what keyword-based search does). When combined with the NLP (Natural Language Programming) model, the search engine will convert each word into a numerical vector that captures its meaning in the context of the search query.
For example, the word "black" might be represented as a vector that points towards other words that are often associated with the color black, such as "dark", "night", and "shadow". Similarly, the word "leather" might be represented as a vector that points towards other words that are often associated with leather, such as "cowhide", "supple", and "luxurious".
The search engine then combines these word vectors to create a representation of the entire search query as a single vector in a high-dimensional space. This vector captures the overall meaning of the search query in a way that goes beyond just the individual words.
Now, the search engine can compare the search query vector to the vectors that represent each product in the store's catalog. By using a distance metric, such as cosine similarity, the search engine can identify the products that are most semantically similar to the search query.
Generating vector embeddings
Vector embeddings are generated using machine learning techniques, specifically neural network models that are trained on large amounts of text data. The most common types of neural network models used to generate vector embeddings are Word2Vec.
Word2Vec is a popular model algorithm used in natural language processing to create numerical representations, or embeddings, of words based on their context. The idea behind Word2Vec is to use a neural network to predict the context of a word based on its neighboring words. This means that the algorithm learns to associate words that appear in similar contexts, resulting in embeddings that encode semantic and syntactic relationships between words.
Vector search in modern retail
When used effectively, vector search can offer multiple opportunities to elevate the ecommerce search experience. Let's explore some of the ways vector search (when combined with other search technologies and methodologies) can deliver powerful search performance -
1) Semantic search
Semantic search is driven by the NLP model and vector search to return search results that are closely related to the user's intent, even if the query does not contain the exact keywords.
For instance, if a user searches for "blue dress for a summer wedding", a semantic search engine can understand that the user is looking for a dress that is suitable for a summer wedding and is blue in color. The search engine might surface listings for "midnight blue dress for a summer wedding", such as "sky blue summer dress", "blue floral dress" depending on the semantic closeness and other factors (such as custom filters).
2) Image search
By using vector search in image search, ecommerce retailers can enable consumers to find visually similar products that match their style preferences, even if they do not have a specific keyword in mind. For instance, a user looking for a red handbag can upload a photo of a handbag they like and use image search to find similar products in the ecommerce store's database. Vector search algorithms can then compare the uploaded image to other images in the product catalog and return a list of handbags threlatedness between products, vector search algorithms can recommend similar products to users, even if they have not explicitly searched for them. For example, a customer browsing for a leather jacket may also be interested in other leather products, such as shoes, belts, or handbags. By analyzing the customer's browsing and purchase history, vector search algorithms can recommend these products to the user, driving cross-selling and upselling opportunities.
3) Synonym detection/intelligent search
Vector search can help retailers identify search queries that do not match any product descriptions or tags, and suggest alternative search terms that may be more relevant to the customer's needs. This can help reduce the number of zero results searches and improve customer satisfaction.
Intelligent search powered by vector search algorithms can also help ecommerce retailers better understand customer behavior and preferences, by analyzing the language and terms used in search queries. This can provide valuable insights into customer needs and preferences, which can be used to improve product offerings and marketing strategiesat match the user's style preferences.
4) Similar products
Vector search algorithms can analyze vast amounts of data points to identify patterns in customer behavior and product attributes. By understanding the semantic
Unlocking search performance with the right search strategy
Whether it's improving search relevance, enabling semantic search, or providing more personalized product recommendations, hybrid search can help ecommerce retailers stay competitive in a rapidly evolving industry. By leveraging the latest search tools and technologies, retailers can create search experiences that meet the needs of their customers and drive increased engagement and sales.
One such solution is Zevi's intelligent search and discovery platform for online businesses and enterprises. Zevi leverages transformational technologies like NLP and advanced machine learning algorithms to deliver highly personalized, relevant and strategic search results for an intuitive and intelligent search experience.
Book a free trial today to unlock your site's search potential.