BM25 or TF‑IDF? Find Out Which Drives Better Search Results

Running an e-commerce store or managing a business website means ensuring customers find the right products or information instantly. But have you ever wondered how your search engine decides which results appear first? 

That’s where search ranking algorithms come into play. They determine the relevance of a page or product to a user’s search query. Two widely used ranking methods—TF-IDF and BM25—stand out in search relevance and information retrieval. 

TF-IDF (Term Frequency-Inverse Document Frequency) prioritizes words based on how often they appear in a document compared to their frequency across multiple documents. While effective, it doesn’t always account for variations in document length or keyword saturation. BM25 (Okapi BM25) improves upon TF-IDF by introducing document length normalization and reducing the influence of overly repeated terms, leading to more balanced and accurate search rankings. 

So, which one is better for e-commerce search, internal site search, or SEO? And how do you decide which to use? In this blog, we’ll break it all down — clear, practical, and to the point. Let’s dive in!

Table of Contents

• Understanding TF-IDF: How It Helps Rank Search Results
• Understanding BM25: A Smarter Approach to Search Ranking
• TF‑IDF vs. BM25: Which One Wins the Search Ranking Battle?
• Real-Life Applications of BM25 & TF‑IDF: Where Do They Shine?
• Practical Implementation Tips for Developers
• Which Algorithm Best Suits Your Business?
• Future of Search: The Rise of the Semantic Layer & AI-Driven Ranking
• Conclusion

Understanding TF-IDF: How It Helps Rank Search Results

TF‑IDF, short for Term Frequency-Inverse Document Frequency, is a ranking algorithm that helps search engines determine how relevant a word is within a document compared to an entire dataset. Instead of treating all words equally, TF‑IDF assigns more importance to terms that are highly specific to a document and less importance to common words that appear everywhere. This method ensures that when a user searches for something, more relevant results appear higher in the rankings, while generic, less useful results are pushed further down.

To visualize how TF‑IDF works, think about the difference between a commonly used word and a highly specific term. Words like “the,” “and,” or “is” appear in nearly every document, so they don’t provide much value in distinguishing one page from another. On the other hand, a phrase like “organic coffee beans” in a product description is much more unique and relevant in an e-commerce search. TF‑IDF recognizes this distinction, ensuring that when someone searches for “organic coffee beans”, pages that truly focus on that topic rank higher than pages where those words appear only once or twice in passing.  

But how does it do this? Let’s break it down into its two key components.

1. Term Frequency (TF) – How Often Does a Word Appear? 

Imagine you have a document—let’s say it’s a product description for a laptop. 

If the word “battery” appears 10 times in a document that contains 500 words, its Term Frequency (TF) would be: 

   

   

That means 2% of the words in this document are “battery.” 

The higher the term frequency, the more relevant the word seems to that document. But there’s a problem. Some words—like “the,” “and,” or “is”—appear frequently in every document. Should they be considered “important”? Not really. That’s why TF alone isn’t enough to rank search results. 

2. Inverse Document Frequency (IDF) – How Rare is a Word? 

TF tells us how often a word appears in a single document. IDF does the opposite—it checks how common or rare that word is across all documents in the database (also called a corpus). 

The formula for IDF is:

   

Where: 

• N = Total number of documents in the corpus 
• DF = Number of documents containing the word 

Let’s say there are 1,000 product descriptions in an e-commerce store. 

• The word “battery” appears in 200 product descriptions 
• The word “the” appears in all 1,000 documents 

We calculate IDF for both words: 

   

   

The result? 

• The word “battery” has a higher IDF, meaning it’s valuable for ranking search results. 
• The word “the” gets a score of 0, meaning it’s ignored in search rankings. 

IDF lowers the importance of common words that appear in too many documents.

Putting it Together – The TF‑IDF Formula 

Now that we have TF (how often a word appears in a document) and IDF (how rare the word is across documents), we multiply them to get the final TF‑IDF score:

   

For the word “battery” in our laptop description:

   

For the word “the”:

   

The higher the TF‑IDF score, the more important that word is for search ranking.

Why TF‑IDF Matters for Search Ranking 

Search engines need a way to determine which results are most relevant when a user searches for something. TF‑IDF plays a key role by ranking documents based on how important a word is within a dataset. It helps: 

• E-Commerce Search – Ensures customers find the most relevant products. 
• Internal Website Search – Improves access to business documents and knowledge bases. 
• Filtering Out Common Words – Prevents generic terms (like “the” or “is”) from affecting search rankings. 

But while TF‑IDF is effective, it has some key limitations that impact its accuracy. 

Limitations of TF‑IDF in Search Ranking 

No Document Length Normalization: A longer product description may rank lower than a shorter one, even if it contains more relevant information. 

Doesn’t Handle Keyword Saturation Well: If a keyword appears 50 times, does that mean the document is 50 times more relevant? Probably not. TF‑IDF lacks a proper weighting system for excessive keyword repetition. 

Relies on Exact Word Matches: Doesn’t recognize synonyms or related words (e.g., it treats “laptop” and “notebook” as completely different terms). Struggles with semantic search, making it less effective for natural language queries. 

Understanding BM25: A Smarter Approach to Search Ranking 

While TF‑IDF is a solid foundation for search ranking, it has key limitations—it doesn’t adjust well for document length and treats repeated words with equal weight, which can lead to less accurate rankings. This is where BM25 (Best Match 25) steps in. BM25 refines TF‑IDF by handling keyword saturation (so a word appearing 50 times doesn’t automatically mean higher relevance) and normalizing document length, ensuring longer documents aren’t unfairly ranked lower. 

These improvements make search results more relevant, balanced, and accurate, providing a better experience for users. Now, let’s break down how BM25 works and why it’s considered the gold standard in search ranking.

1. BM25 – The Next Step in Search Ranking 

BM25 is an evolution of TF‑IDF. It still uses Term Frequency (TF) and Inverse Document Frequency (IDF), but with modifications to improve ranking accuracy. 

The two biggest upgrades BM25 brings: 

• Document length normalization – It ensures longer documents aren’t unfairly ranked lower. 
• Keyword saturation control – It prevents words from becoming too influential if they appear too often. 

The goal? More accurate and balanced search relevance. 

2. The BM25 Formula (Don’t Worry, We’ll Simplify It!) 

The core BM25 formula looks like this:

   

Let’s break it down piece by piece. 

Inverse Document Frequency (IDF) – Importance of the Term 

Just like in TF‑IDF, BM25 uses IDF to lower the importance of very common words: 

   

Where: 

• N = Total number of documents in the corpus 
• DF(t) = Number of documents containing the word 

This ensures that rare words (like “processor” in a laptop search) get more weight, while common words (like “the” or “and”) get less weight. 

Term Frequency (TF) – How Often Does the Word Appear? 

In TF‑IDF, term frequency is counted directly. But in BM25, term frequency is adjusted to prevent excessive repetition from inflating the ranking.

   

Where k1 is a tuning parameter that controls how fast the word frequency “saturates.” 

• If k1 is high, BM25 acts more like TF‑IDF (word frequency is important). 
• If k1 is low, extra occurrences of a word contribute less and less to ranking. 

For example: 

• If the word “battery” appears once in a document, it helps the ranking a lot. 
• If it appears 50 times, it doesn’t mean the document is 50× more relevant—BM25 scales it down so it doesn’t dominate. 

Document Length Normalization – Fairness Between Short & Long Documents 

One big issue with TF‑IDF is that long documents tend to have higher word counts just because they contain more text. This can distort search ranking unfairly. 

BM25 fixes this by introducing document length normalization: 

   

Where: 

• b is a tuning parameter (usually 0.75 by default). 
• |D| is the number of words in the document. 
• avgdl is the average document length in the entire dataset. 

How does this help? 

• If a document is longer than average, BM25 lowers the importance of frequent words. 
• If a document is shorter than average, BM25 keeps the importance of frequent words. 

This ensures that long and short documents compete fairly in search rankings. 

3. How BM25 Solves TF‑IDF’s Weaknesses

Problem with TF‑IDF	How BM25 Fixes It
Overvalues very frequent words	Keyword saturation reduces over-weighting
Long documents rank lower unfairly	Document length normalization balances the scores
Doesn’t adjust for word importance drop-off	Words contribute less impact after a few appearances
Linear scaling of term frequency	BM25 uses controlled frequency scaling

4. BM25 and TF‑IDF – Features provided

Features	TF-IDF	BM25
Word Frequency Scaling	Direct count (linear)	Saturation (non-linear)
Document Length Adjustment	None	Normalized
Control Overweighting of Repeated Words?	No	Yes
Better for Long & Short Documents?	No	Yes
Used in Modern Search Engines?	Limited	Yes (default in Elasticsearch, Azure, etc.)

BM25, the default ranking algorithm for modern search engines (e.g., Elasticsearch, Solr, Azure Cognitive Search), is a smarter version of TF-IDF. It improves search relevance by handling keyword saturation and document length. A comparison of TF-IDF and BM25, including business use cases, follows. 

TF‑IDF vs. BM25: Which One Wins the Search Ranking Battle?

Now that we’ve explored TF‑IDF and BM25 separately, how do they stack up against each other? While both are ranking algorithms used to determine search relevance, they work differently. TF‑IDF focuses on term importance within a dataset but struggles with document length normalization and keyword saturation. BM25 improves on this by introducing dynamic term weighting, ensuring longer documents aren’t unfairly penalized and preventing keyword stuffing from manipulating rankings. So, when should you choose BM25 over TF‑IDF?  

If you need more precise search ranking, better handling of natural language queries, and improved scalability—BM25 is the better choice. Let’s break down their differences in more detail.

Impact on Search Relevance

A search engine’s goal is to rank documents accurately based on user intent. BM25 takes a more refined approach, making search results more relevant in real-world scenarios. 

Challenge	How TF-IDF Behaves	How BM25 Solves It
Repeated words (Keyword Saturation)	More repetitions = Higher ranking (can be exploited)	Extra repetitions contribute less impact (fairer ranking)
Document Length Normalization	Long documents get lower scores (unfair)	Adjusts ranking based on document length
Short vs. Long Documents	Shorter documents often rank higher	Ensures balance between both
Common Business Words (e.g., “best,” “top”)	Can appear too frequently, skewing results	Lower weighting for overused terms

What This Means for You 

• If one document repeats a keyword 100 times, TF‑IDF gives it a huge advantage. 
• BM25 smooths this out, ensuring better ranking fairness. 

Practical Business Applications – When to Use Each One? 

Use Case	TF-IDF	BM25
E-Commerce Product Search	Not Ideal – Can over-rank products with excessive keyword stuffing	Better – Weights product descriptions fairly
Website Internal Search	Can make long documents rank lower	More balanced rankings for FAQs, blog posts, reports
SEO (Search Engine Optimization)	Used in basic keyword analysis	Used in advanced search relevance tuning
Small Datasets (Few Documents)	Simple and works well	Also works well, but tuning required
Large-Scale Search (e.g., enterprise search, AI-driven search engines)	Limited	Preferred algorithm (used by Elasticsearch, Azure, Solr)

What This Means for You 

• TF‑IDF is simple and works for small projects or basic keyword extraction. 
• BM25 is better when search accuracy matters, especially in e-commerce and website search. 

Which One Should You Choose? 

Choosing between TF‑IDF and BM25 depends on your specific needs. If you’re looking for a quick, lightweight ranking algorithm, TF‑IDF works well for simple applications. However, if you want more accurate, real-world search relevance, BM25 is the smarter choice. That’s why it has become the default ranking algorithm in most modern search engines, including: 

• Elasticsearch 
• Azure Cognitive Search 
• Apache Solr 

For better scalability, precision, and handling of natural language queries, BM25 is the way forward. 

Real-Life Applications of BM25 & TF‑IDF: Where Do They Shine?

Now that we’ve explored how BM25 and TF‑IDF work, let’s see how they apply to real-world business scenarios. From e-commerce product searches to website internal search and SEO strategies, choosing the right ranking algorithm can make or break user experience and search accuracy.

E-Commerce Search – Ranking Products Effectively 

When customers visit an e-commerce site, they expect instant, relevant results for searches like “best running shoes” or “affordable winter jackets.” The key is to make sure the most relevant products appear at the top of the search results. 

How BM25 Helps: 

• BM25 helps rank products more fairly by adjusting for document length and keyword saturation. 
• It gives more weight to relevance and context, ensuring products that truly match a customer’s search intent are ranked higher. 

How TF‑IDF Works: 

• While TF‑IDF is simpler and might work for smaller e-commerce sites, it tends to overweight repeated keywords in product descriptions. This can cause irrelevant results to appear higher in search rankings. 

For small e-commerce sites, TF‑IDF may suffice. But as your site grows and your catalog expands, BM25 becomes a more robust option to provide more accurate and relevant search results. 

Website Internal Search – Improving User Experience 

For business websites, having a strong internal search system is essential—whether it’s for a blog, knowledge base, or FAQ section. Employees, customers, or visitors should be able to find what they need quickly and effortlessly. 

How BM25 Helps: 

• BM25 provides better search ranking accuracy by normalizing document length. 
• It also helps in handling keyword variations, so when users search for terms like “customer service” or “support,” both get appropriately ranked based on context, not just keyword frequency. 

How TF‑IDF Works: 

• TF‑IDF can sometimes struggle with content that has varying keyword density or long-form content, making it harder to determine the true relevance of a document. 
• Short articles may rank higher even if they don’t fully answer the query, simply by repeating the search term more often. 

For content-heavy sites like blogs or help centers, BM25 enhances search accuracy by prioritizing relevance over keyword stuffing, ranking longer, more useful articles higher. 

SEO – Boosting Your Website’s Search Visibility 

Search engines like Google use ranking algorithms similar to BM25 and TF‑IDF to determine how well your content ranks. The way your pages are ranked directly impacts SEO and visibility. 

How BM25 Helps: 

• BM25 enhances SEO by prioritizing relevance over keyword frequency, adjusting for real-world searches and normalizing content length for better rankings. 
• It helps ensure your pages are ranked fairly, even if they’re long-form articles or contain multiple related keywords. 

How TF‑IDF Works: 

• TF‑IDF can be useful for basic SEO strategies, helping you understand how relevant your content is to certain keywords. 
• TF‑IDF aids basic SEO by measuring keyword relevance but doesn’t adjust for diminishing returns, risking keyword overuse. 

Practical Implementation Tips for Developers

Choosing between TF‑IDF and BM25 for your search system depends on your project size, complexity, and search accuracy needs. Here’s how you can implement them effectively based on your requirements. 

For Small Projects: Keep It Simple with TF‑IDF 

If you’re building a basic search system or working with a small dataset, TF‑IDF is a great starting point because: 

• It’s simple and lightweight, requiring minimal setup. 
• It efficiently ranks search results, making it useful for basic applications. 
• It’s easy to implement, especially for projects that don’t need complex ranking logic. 

Best For: Small websites, blogs, and projects where exact word matches are enough. 

For Large-Scale or Complex Search Systems: Choose BM25 

If you’re working on a large e-commerce site, an internal knowledge base, or a multi-layered search system, BM25 is the smarter choice. Why? 

• It handles large datasets better, adjusting for document length and keyword saturation. 
• It ranks search results more accurately, improving user experience and relevance. 
• It adapts well to natural language searches, ensuring better precision over time. 

Best For: E-commerce product search, knowledge bases, and large-scale content repositories. 

Tuning BM25: Adjusting for Your Needs 

BM25 comes with two key parameters that can be fine-tuned for better ranking performance: 

•  k₁ (Term Saturation Control) 
  • Controls how much weight is given to repeated words in a document. 
  • Higher values mean more weight is given to repeated terms.
  • Lower values prevent keyword stuffing from unfairly influencing ranking. 

• b (Document Length Normalization) 
  • Controls how much influence document length has on ranking.
  • b = 1 favors shorter documents more.
  • b = 0 treats all documents equally, regardless of length.

Best Practice: Fine-tune k₁ and b based on your dataset and search needs to get the best balance between precision and recall. 

Libraries & Tools for Implementing BM25 & TF‑IDF 

There are several powerful libraries available that support both BM25 and TF‑IDF: 

• Elasticsearch – Uses BM25 as the default ranking algorithm, ideal for scalable search applications. 
• Whoosh (Python Library) – Supports both TF‑IDF and BM25, perfect for lightweight search implementations. 
• Lucene (Java-Based Search Engine) – A robust search library used in high-performance applications. 

Which One to Choose? If you’re building a production-ready search system, Elasticsearch is the best choice due to its scalability, speed, and default BM25 integration. 

Which Algorithm Best Suits Your Business? 

Choosing between BM25 and TF‑IDF depends on your content size, search complexity, and relevance needs. Let’s break it down to find the best fit for your business. 

Choosing TF‑IDF: A Simple Solution for Smaller or Less Complex Needs 

If your business has smaller content volumes or simpler search needs, TF‑IDF can be a great choice. Here’s why: 

• Smaller websites or blogs – Ideal for sites with fewer pages, as it’s simple and effective without needing BM25’s advanced features. 
• Low complexity – Works well for basic searches in product descriptions, articles, or small knowledge bases. 
• Less frequent updates – Suitable if your content remains largely unchanged, requiring minimal search ranking adjustments. 

Choosing BM25: A More Powerful Tool for Larger or Complex Sites 

On the other hand, if your business is scaling or you need more advanced search capabilities, BM25 is the better option. Here’s when you should consider BM25: 

• E-commerce websites – Ideal for large product catalogs, handling complex searches and ranking results based on relevance, not just keyword count. It adjusts for keyword saturation, ensuring that long product descriptions don’t dominate results just because they have more keywords. 
• Large content repositories – Best for knowledge bases, academic sites, or corporate documentation, ensuring accurate rankings across extensive content. 
• Frequent updates – Keeps search results relevant by adapting to constant content changes, preventing outdated pages from ranking higher. 
• Improved relevance – Supports advanced internal search with custom filtering, sorting, and fine-tuned ranking adjustments for better user experience. 

SEO Impact 

Search Engine Optimization (SEO) also plays a role in your decision. If your business heavily relies on organic search traffic (through search engines like Google), then BM25 may offer a better ranking system. It fine-tunes results, increasing the search relevance and improving how your pages rank for specific keywords. 

For internal search (like on an e-commerce site), you want high relevance and a system that adjusts for keyword frequency and content length. Again, BM25 shines here, balancing search relevance with document length normalization. 

Your Budget and Resources 

Finally, consider your available resources. TF‑IDF is easier to implement and doesn’t require a lot of computational power. If you work with limited resources or a minor development team, TF-IDF might be the way to go. 

On the other hand, BM25 is more complex. It requires more computing power and often fine-tuning of parameters. So, if you’re working on a large, high-traffic site with dedicated developers and the necessary infrastructure, BM25 will deliver the best results. 

Future of Search: The Rise of the Semantic Layer & AI-Driven Ranking 

Search technology is evolving beyond basic keyword matching. The semantic layer is reshaping how search engines understand intent, context, and relationships between words — making searches smarter and more relevant. Instead of relying only on TF‑IDF or BM25, the semantic layer enhances traditional ranking models by adding deeper meaning and context recognition. Let’s explore how this transformation is changing search ranking for businesses. 

What is the Semantic Layer? 

Unlike BM25 and TF‑IDF, which rank content based on word frequency and document structure, the semantic layer interprets the actual meaning behind a user’s search query. This approach ensures that users get results based on intent, not just keyword matches. 

• Understands word relationships instead of just counting occurrences. 
• Bridges the gap between user queries and the most relevant content. 
• Delivers better results by recognizing synonyms, variations, and context. 

Example: Instead of just matching “affordable laptops,” the semantic layer understands intent and retrieves results for “budget-friendly notebooks” or “best low-cost computers”—even if those exact words aren’t used in the query. 

How the Semantic Layer Work with BM25 and TF‑IDF? 

The semantic layer doesn’t replace BM25 or TF‑IDF. Instead, it works alongside them to improve search relevance. 

• Enhancing BM25 – BM25 adjusts rankings based on document length, but the semantic layer adds context, improving relevance by understanding word meanings. 
• Enhancing TF‑IDF – While TF‑IDF ranks documents by keyword occurrence, the semantic layer interprets synonyms and related terms, ensuring broader and more accurate search results. 

The Rise of Machine Learning & Semantic Search 

Advancing machine learning enhances semantic search, enabling algorithms to understand query context and deliver relevant results without relying on exact keywords—a game-changer for e-commerce and internal site search. 

• E-Commerce Search – Instead of relying on exact keywords, semantic search understands intent, recommending related items like summer shoes or swimwear for a “summer dresses” query. 
• Website Internal Search – Improves results by recognizing intent, suggesting relevant articles, guides, or tutorials for queries like “how to set up a project,” even without exact keyword matches. 

Machine Learning’s Impact on Search Algorithms 

The future of search lies in machine learning. Unlike BM25 and TF‑IDF, these models evolve by learning from user behavior, making search engines smarter over time and requiring businesses to adapt their strategies. 

For example: 

• User Behavior Learning – Tracks user clicks on search results, prioritizing frequently clicked content for better future rankings and relevance. 
• Optimized Ranking Algorithms – Goes beyond keyword frequency and document length, factoring in user engagement and page quality for more accurate search rankings. 

What Does the Future Hold for Search Ranking? 

As businesses continue to shift to semantic search and machine learning, ranking algorithms will become more sophisticated. Here are a few trends we can expect: 

• More Intuitive Search – Algorithms will understand query meaning, improving relevance and user experience with faster, more efficient results. 
• Increased Personalization – Search results will adapt to user preferences, history, and location, enhancing e-commerce with tailored recommendations. 
• Better Handling of Ambiguity – Semantic understanding will interpret context, distinguishing between terms like “apple” (fruit or tech) based on user behavior. 

Incorporating SEO in the Semantic Layer 

As the semantic layer evolves, SEO will need to adapt. Content will need to be optimized not just for keywords but also for context and intent. Businesses will need to focus on: 

• Contextual Relevance – Focus on aligning content with user intent rather than just keyword matching. 
• Content Quality – Prioritize high-quality, informative content that directly meets user needs. 
• Rich Content – Utilize structured data like schema markup to help search engines better understand content context. 

As AI, machine learning, and semantic search continue to evolve, businesses that embrace these changes will create more relevant, engaging, and high-ranking content—keeping them ahead in the digital landscape. 

Conclusion 

Choosing between TF-IDF and BM25 depends on your search needs. TF-IDF provides a solid foundation for ranking by emphasizing word importance, but BM25 refines this approach with better handling of document length and keyword saturation. For most modern search applications—e-commerce, internal site search, or SEO—BM25 often delivers more relevant and user-friendly results. 

Ultimately, the right choice comes down to balancing precision, scalability, and user expectations. Understanding these ranking models helps businesses optimize search performance and improve the overall user experience. By leveraging the right algorithm, you ensure faster, smarter, and more effective search results—helping users find exactly what they need.