What Is Semantic Search?<\/h2>\n
But what is semantics, exactly? Well, it refers to the philosophical study of meaning. (Pretty deep, huh?) Even though philosophy doesn’t always relate to software engineering, it does in this instance, meaning the definition helps us better understand the concept at hand.<\/span><\/p>\n Indeed, “semantic search” is synonymous with understanding your customer or buyer and is made possible through a combination of <\/span>natural language processing (NLP)<\/span><\/a> and machine learning (ML). Semantic search leverages vector embeddings and neural networks to transform words and phrases into mathematical representations that capture their meaning and relationships to other concepts.<\/span><\/p>\n In fact, these types of artificial intelligence make search results more comprehensive, seamless, and meaningful by understanding exactly what the prospect is trying to ask and what the user intent is behind the arrangement of keywords, as opposed to simply matching keywords to web pages. This helps deliver the most relevant, revenue-driving products based on how people search and browse.<\/span><\/p>\n <\/p>\n <\/p>\n Most search intent can fall under the following categories: informational, navigational, commercial, or transactional. Being in commerce, your brand is most likely focused on the informational and transactional above all else.<\/span><\/p>\n It’s up to your ecommerce business to equate a customer’s intent behind a query to the context of a specific keyword or keyword phrase with <\/span>your search engine<\/span><\/a>. Humans rely on this very context in the real world when speaking and interacting.<\/span><\/p>\n If I were to ask you, “Do you like this article?”, and then followed up with, “What do you like about it?”, you would know that “it” refers to the article based on the direction of our conversation. The impact of context in our daily conversations is endless because it’s often linked to what has been previously said or what followed.<\/span><\/p>\n The setting and time in which a conversation takes place, the background knowledge of those involved in the discussion, and the type of relationship that they have established all play a role in how we communicate with one another. Search is no different. Think of these contextual attributes as what semantic search is emulating.<\/span><\/p>\n That’s the reason why local restaurants appear when you type “restaurants” into Google. The context of who you are, where you are, and what you want are all understood by the search engine.<\/span><\/p>\n Search relevance<\/span><\/a>, or a search engine’s ability to match a page of search results to user intent, isn’t possible without semantic understanding. NLP helps your search bar understand your products on a deeper level, while ML uses data and trial-and-error patterns to enhance user experience.<\/span><\/p>\n Behind every semantic search system lies a sophisticated technical architecture built on vector embeddings and transformer models. When a customer types a query like “comfortable summer dress,” the system doesn’t just look for those exact words. Instead, it converts both the query and product descriptions into high-dimensional mathematical vectors that capture semantic meaning.<\/span><\/p>\n These vector embeddings are created using transformer models like BERT (Bidirectional Encoder Representations from Transformers) or sentence transformers, which have been trained on vast amounts of text to understand language relationships. The system then uses techniques like cosine similarity to measure how closely related a search query is to different products in the catalog, even when they don’t share identical keywords.<\/span><\/p>\n For example, a query for “breathable fabric” might match products described as “moisture-wicking” or “lightweight cotton” because the embedding model understands these concepts are semantically related. This is powered by k-nearest neighbor (kNN) search algorithms that can quickly find the most similar product vectors to a given query vector, all happening in milliseconds.<\/span><\/p>\n Modern semantic search systems also integrate with large language models (LLMs) through techniques like Retrieval Augmented Generation (RAG), where the search system first retrieves relevant products, then uses an LLM to understand complex, conversational queries or provide more detailed product recommendations based on nuanced customer needs.<\/span><\/p>\n Again, semantic search adds a deeper level of understanding to search intent, as the algorithms continue learning through bounce rates, conversion rates, and other types of key performance indicators (KPIs). By solving the “search quality problem,” these algorithms can boost user satisfaction and brand loyalty.<\/span><\/p>\n Understanding how semantic search compares to other search methodologies helps clarify its unique value in ecommerce environments.<\/span><\/p>\n Semantic vs Keyword Search<\/b>: Traditional keyword search relies on exact word matching, where “red shoes” will only return products containing those specific terms. Semantic search understands that “crimson footwear” or “burgundy sneakers” might be what the customer actually wants, even without exact keyword matches.<\/span><\/p>\n Semantic vs Lexical Search<\/b>: Lexical search focuses on the literal meaning of words and their dictionary definitions. Semantic search goes deeper, understanding context, synonyms, and conceptual relationships. When someone searches for “business attire,” lexical search looks for those exact words, while semantic search understands they might want suits, blazers, dress shirts, or professional accessories.<\/span><\/p>\n Semantic vs Vector Search<\/b>: This distinction is more technical. Vector search refers to the mathematical approach of comparing numerical representations of data. Semantic search is the application of vector search specifically for understanding meaning and intent. All semantic search systems use vector search, but not all vector search is semantic.<\/span><\/p>\n\n
How Does Contextual Meaning Impact Semantic Search?<\/b><\/h2>\n
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<\/p>\nHow Does Semantic Search Work?<\/b><\/h2>\n
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<\/p>\nThe Technical Foundation<\/b><\/h3>\n
Semantic Search vs Other Search Types<\/b><\/h2>\n
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<\/p>\n![\"Conventional](\"https:\/\/www.bloomreach.com\/wp-content\/uploads\/2024\/05\/conventional-online-search-compared-to-bloomreach-online-search-machine-screw-vs.-screw-machine-1.jpg\")
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