What Is BM25 (Best Match 25): Full Breakdown - Luigi’s Box
Excerpt
BM25 (Best Match 25) ranks search relevance using term frequency and document length. Learn how this AI-driven algorithm is applied in e-commerce.
Glossary
BM25 is a ranking function measuring relevance based on term frequency and document length.
In the ever-evolving landscape of e-commerce and digital platforms, the ability to provide users with highly relevant search results is crucial. Best Match 25 is an innovative algorithm that optimizes search accuracy and user satisfaction.
This article will delve into the intricacies of Best Match 25, exploring how it enhances search functionalities and why it stands out as a superior choice for modern search needs.
What is BM25
BM25, or Best Match 25, also known as Okapi BM25, is a ranking algorithm for information retrieval and search engines that determines a document’s relevance to a given query and ranks documents based on their relevance scores.
How does BM25 work?
The BM25 retrieval function calculates a relevance score for each document based on a specific search query.
The algorithm looks at three things:
- How often do the query terms appear in the document.
- The length of the document.
- The average length of all documents in the collection.
A diagram showing the workings of the BM25 algorithm
The formula uses two adjustable parameters, 𝑘1 and 𝑏 to control how much term frequency and document length affect the score.
Key components of the BM25 algorithm
Let’s go over the most critical components that make up the BM25 formula.
- Term frequency (TF): The frequency of a term in the document. The more times a term occurs in a document, the higher its TF value.
A graphical representation of the impact of term frequency saturation
Source
- Inverse document frequency (IDF): This measures the rareness of the search term in the entire collection of documents. Rare terms receive higher IDF values, encouraging the document retrieval algorithm to prioritize them.
- Document length (DL): The number of words in the document. Longer documents are penalized to avoid favoring them over shorter documents.
- Average document length (AVDL): The average document length across the entire collection. It helps in normalizing the document length across the corpus.
What are its advantages and disadvantages?
BM25 offers advantages such as:
- Dynamic ranking: Unlike the static nature of TF-IDF, BM25 adjusts its ranking based on the distribution of terms within the collection, making it more adaptable to different types of documents and queries.
- Effective for long queries: The ranking function tends to perform better than TF-IDF for longer queries as it addresses the issue of term saturation and considers the overall document length.
Although BM25 is a powerful ranking algorithm, it also has some limitations:
- No semantic understanding: BM25 does not consider the semantic meaning of the query terms or the documents, which means it may not be able to capture the full context of the search.
- No personalization: BM25 treats all users’ queries equally, which may not provide personalized results for individual users.
Where can you find this algorithm?
BM25 algorithm can be found and applied in various domains where information retrieval and search functionality are required. Here are some common areas:
1. Web search engines
Many popular web search engines, like Google, Bing, or Yahoo, employ BM25 or similar ranking algorithms to determine the relevance of search results for a given query.
2. Enterprise search systems
In large organizations, enterprise search systems use BM25 to provide employees with relevant documents, files, and information from internal databases.
3. E-commerce websites
Online shopping platforms often use BM25 or similar algorithms to rank products based on their relevance to users’ search queries and provide personalized product recommendations.
4. Question-answering systems
BM25 can be employed in question-answering systems to rank potential answers based on their relevance to the query.
5. Recommendation systems
In recommendation engines, BM25 can be used to rank items or content according to user preferences or interests.
6. Text mining and information extraction
BM25 can aid in extracting relevant information from large text datasets during text mining and information extraction tasks.
Conclusion
BM25 is a powerful ranking algorithm and valuable tool for enhancing search relevance and delivering more accurate and useful user results.
It’s also important to note that while BM25 is a widely used and effective ranking algorithm, its usage and application might vary depending on the specific requirements and characteristics of the system or application.