The most important ranking methods for modern search engines

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Modern search engines can rank search results in different ways. Vector Ranking, BM25, and Semantic Ranking are all methods used in information retrieval and search engines to rank and retrieve documents or pieces of content based on their relevance to a query.

Each of these methods represents a distinct paradigm in the way search relevance is determined.

BM25, a traditional and widely-used algorithm, excels in scenarios where keyword matching and simplicity are paramount.

Vector Ranking, leveraging the geometric relationships between words in a high-dimensional space, offers a more nuanced approach to document similarity.

Meanwhile, Semantic Ranking, driven by the latest advancements in natural language processing, seeks to understand the deeper meaning behind queries, making it indispensable for complex, context-rich search tasks.

Understanding these ranking techniques is essential for anyone involved in developing or optimizing search and retrieval systems. Whether you’re designing a search engine, building a content recommendation system, or enhancing user interactions with AI, knowing when and how to apply BM25, Vector Ranking, or Semantic Ranking can significantly impact the effectiveness of your solution.

Contents

1 BM25
2 Vector Ranking
3 Semantic Ranking
4 How Hybrid Ranking Solutions Work

BM25

What is it? BM25 is a probabilistic-based ranking function, part of the family of “bag-of-words” retrieval models. It calculates the relevance of a document to a query by considering factors like term frequency (how often a term appears in the document), inverse document frequency (how common or rare a term is across all documents), and document length normalization.

How does it work?

Term Frequency (TF): More occurrences of a term in a document make it more relevant.
Inverse Document Frequency (IDF): Rarer terms are more informative and thus have more weight.
Document Length Normalization: Shorter documents are favored because they are more likely to be concise.

When to use it?

Keyword-based searches: BM25 is very effective for traditional keyword-based search, especially in scenarios where precision and recall are important.
Low computational cost: It’s relatively lightweight and fast, making it ideal for large-scale search engines where speed is crucial.

More info about BM25 in detail.

Vector Ranking

Vector ranking refers to the use of vector space models (VSM) for information retrieval. In this method, both documents and queries are represented as vectors in a multi-dimensional space. The relevance of a document to a query is determined by the cosine similarity between their vectors.

How does it work?

Vector Representation: Each word is represented as a dimension, and documents are vectors in this multi-dimensional space.
Cosine Similarity: The angle between the query vector and document vector determines the relevance; smaller angles (closer vectors) indicate higher relevance.

When to use it?

Document similarity searches: Useful for finding documents that are similar to a query or to another document.
Content-based recommendation: It’s effective in content recommendation systems where you need to find items (like articles or products) that are similar to a given input.

Semantic Ranking

What is it? Semantic ranking is a more advanced method that considers the meaning of words and phrases, rather than just their literal occurrence in the text. It often leverages deep learning models like BERT (Bidirectional Encoder Representations from Transformers) or other NLP models that can understand context and semantics.

How does it work?

Pre-trained Language Models: Models like BERT are trained on vast amounts of data to understand context and semantic meaning.
Contextual Understanding: Unlike BM25 and simple vector models, semantic ranking can understand the context of words in a query and match it to documents that are semantically similar, even if they don’t share the same keywords.

When to use it?

Natural language queries: Ideal when users search in natural language, using full sentences or questions rather than just keywords.
Complex information needs: Useful in scenarios where understanding the meaning behind a query is critical, such as question-answering systems, conversational AI, and advanced search engines.

How Hybrid Ranking Solutions Work

Combining Different Ranking Models:
- BM25 and Semantic Ranking: A common hybrid approach might involve using BM25 to quickly filter out the most relevant documents based on keyword matching, followed by a more detailed semantic ranking using models like BERT to refine the results. This combination ensures that the search is both fast and contextually accurate.
- Vector and BM25 Ranking: Another example could be using BM25 to rank documents initially, then applying vector ranking to those top-ranked documents to find the most similar ones. This is useful in cases where the search system needs to find content similar to a specific query or document.
Weighting and Scoring Mechanisms:
- In hybrid systems, different ranking models contribute to the final ranking score. For instance, BM25 might contribute 50% to the final score, while a semantic model like BERT could contribute the other 50%. These weights can be adjusted based on the specific needs of the search system or the type of queries being processed.
- Score Aggregation: The system might normalize and combine scores from different models to produce a final ranking. This could involve simple averaging, weighted summation, or more complex methods like machine learning-based score fusion, where the system learns the optimal way to combine scores from different models.
Cascading and Pipelining:
- In some hybrid systems, different ranking models are applied in stages. For example, a cascading approach might first apply BM25 to reduce the number of documents considered, then pass the top results to a more computationally intensive semantic ranking model. This approach ensures efficiency while maintaining high relevance in the final results.
- Pipelining: This involves sequentially applying different models, where the output of one model serves as the input to the next. For example, an initial keyword-based search could identify relevant documents, followed by a vector-based model that re-ranks these documents based on similarity to the query.
Machine Learning-Based Hybrid Models:
- Some advanced hybrid systems use machine learning to dynamically decide how to combine different ranking methods based on the query type or user behavior. These models might be trained on large datasets to learn the optimal way to rank documents, considering multiple factors such as keyword relevance, semantic meaning, and user engagement data.
- Learning to Rank (LTR): This is a technique where a machine learning model is trained to rank documents by learning from historical search data. The model can consider multiple features, including outputs from BM25, vector similarity, and semantic models, to determine the final ranking.

When to Use Hybrid Ranking Solutions

Diverse Query Types: If your search system needs to handle a wide variety of query types—ranging from simple keyword searches to complex natural language questions—a hybrid approach can provide the flexibility needed to address these differences.
Large-Scale Search Systems: For search engines that need to balance speed with accuracy, a hybrid system allows for efficient initial filtering with BM25 and more precise ranking with semantic models.

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About Olaf Kopp

Olaf Kopp is Co-Founder, Chief Business Development Officer (CBDO) and Head of SEO & Content at Aufgesang GmbH. He is an internationally recognized industry expert in semantic SEO, E-E-A-T, modern search engine technology, content marketing and customer journey management. As an author, Olaf Kopp writes for national and international magazines such as Search Engine Land, t3n, Website Boosting, Hubspot, Sistrix, Oncrawl, Searchmetrics, Upload … . In 2022 he was Top contributor for Search Engine Land. His blog is one of the most famous online marketing blogs in Germany. In addition, Olaf Kopp is a speaker for SEO and content marketing SMX, CMCx, OMT, OMX, Campixx...