Martin Bayton
Search Engine Algorithms
In this blog we’ll explore search algorithms and how they have evolved in search engines and search applications.
Algorithm [noun]: a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.
Behind the scenes, search engines rely on sophisticated algorithms. These algorithms work together to process user queries, interpret their intent, match them against indexed documents, and rank the search results based on relevance. In this blog we’ll scan through some of the more common algorithms used in traditional search engines then take a look at machine learning algorithms and vector search.
Common Algorithms Used in Search
Let’s start by exploring some algorithms commonly used in keyword search which dominated the search paradigm for decades. Tokenization and term extraction are algorithms frequently used during indexing. Tokenization breaks down documents into individual tokens or terms and creates a structured representation of the text that can be efficiently processed. Then term extraction algorithms analyse these tokens and extract the most important capturing key concepts and words. In conjunction, Term Frequency-Inverse Document Frequency (TF-IDF) algorithms are often used to assign weights to the extracted terms based on frequency and rarity across the corpus, helping rank and prioritize documents that contain these important terms. The terms are then used for indexing.
During query processing, algorithms like query parsing, Boolean logic and fuzzy matching can be found. Query parsing algorithms parse and analyse user queries. They break down the query into individual terms or phrases, handle operators, and transform the query into a structured format that can be processed by the search engine. Boolean algorithms specifically handle Boolean operators within queries and fuzzy matching algorithms handle variations, errors, or misspellings by employing techniques like edit distance, phonetic matching, or approximate string matching.
Probably the best know search related algorithm is BM25 also known as Best Match 25. It is commonly used to determine the relevance and ranking of documents in response to user queries. It works by calculating a relevance score for documents based on factors such as term frequency, document length and inverse document frequency (a measure of how common a term is in the corpus of documents). By considering these factors BM25 ranks and retrieves documents that are most relevant to the user’s query. BM25 is considered to be a more advanced and accurate algorithm for ranking search results compared to TF-IDF.
It’s important to note that the algorithms used at any stage of the search process will vary depending on search engine configuration and requirements. Additionally, many stages involve a combination of algorithms and techniques working together to provide comprehensive search functionality.
Machine Learning Algorithms
All the algorithms we have spoken about so far like BM25 and TF-IDF are deterministic, programmed to follow a predefined set of steps or logical operations to solve a specific problem. On the other hand, a machine learning algorithm is designed to learn patterns and relationships directly from data. Instead of being explicitly programmed, machine learning algorithms learn from examples and iteratively improve their performance over time. They utilize statistical techniques and mathematical models to automatically discover patterns, make predictions, or make decisions based on the provided data. Machine learning algorithms (more specifically language models) like Google BERT and Mini-LM L6 are increasingly being used by search engines to:
- Improve relevance by better understanding user intent.
- Enhance the ranking of search results by augmenting BM25 and/or TF-IDF.
- Boost a search engines’ ability to understand and interpret natural language queries
- Enable advanced search capabilities such as extractive answers
Vector Search
Today, most modern search engines support vector search. Vector search is a technique that involves representing documents or queries as vectors in a high-dimensional space, typically using mathematical models like the vector space model or word embeddings. These vectors capture the semantic meaning and relationships between words or documents, allowing for efficient similarity calculations.
HNSW (Hierarchical Navigable Small World) and cosine similarity algorithms are commonly used together in vector search. HNSW constructs a hierarchical graph structure to efficiently index vectors. Cosine similarity is then used as a distance metric to measure the similarity between query vectors and indexed vectors. HNSW uses this cosine similarity measure during search to navigate through the hierarchical graph, narrowing down the search space and retrieving vectors that are most similar to the query vector.
Vector search offers significant benefits over traditional keyword search by considering the semantic meaning and relationships between words. It improves relevance by matching user intent based on similarity rather than relying solely on exact keyword matches. Additionally, vector search handles synonyms and variations, leading to more accurate search results.
Wrapping Up
Well, that was a whistle-stop tour of some of the common algorithms that are being used by search applications. I hope you found the blog useful and it’s added to your understanding of search technologies. We’ll be taking a closer look at some of the technologies mentioned like vector search, language models and machine learning in future blogs in the Search from A-Z series.
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