Quality Classification vs. Relevance Scoring in search engines

SEOs should know the difference between relevance scoring and quality classification. Relevance scoring is an evaluation of a document that is always directly related to the search queries and it is using points to validate a document. Quality classifiers are using classes or categories for evaluating a document according to a topic, context … Also a classifier can be used to classify domains, website areas, source entities or a query itself.

E-E-A-T is Google’s quality classifier concept, which summarizes the ratings of different quality classifiers. This makes it all the more important to know the differences.

Scorer vs. Classifier

In information retrieval (IR), Scorer and Classifier serve different roles, though both contribute to ranking and decision-making in search and recommendation systems.

Key Differences

Scorer

• A Scorer is responsible for assigning a numerical score to documents based on their relevance to a given query.
• It typically uses features like term frequency (TF), inverse document frequency (IDF), BM25, neural embeddings, and learning-to-rank models.
• The output of a scorer is a ranking of documents, where higher-scoring documents are more relevant.
• Common approaches:
  • Traditional IR models: BM25, TF-IDF, language models
  • Neural ranking models: BERT-based rankers, Siamese networks
  • Learning-to-Rank (LTR): Gradient Boosting Trees (GBMs), neural ranking models

📌 Example: A BM25 Scorer assigns a score to each document based on query-term matching.

Classifier

• A Classifier is responsible for assigning a category or label to a document or query.
• It is usually used in categorization tasks, such as:
  • Spam detection (spam vs. non-spam)
  • Query intent classification (navigational, informational, transactional)
  • Document genre classification (news, blog, research paper)
• It typically uses machine learning models like decision trees, SVMs, deep learning (BERT, CNNs).
• The output of a classifier is a discrete label or class, rather than a ranking score.

📌 Example: A Neural Classifier predicts whether a document belongs to “Sports” or “Politics.”

Scoring Algorithms in Information Retrieval

Term Weighting (TF-IDF): One of the foundational scoring methods in IR is TF-IDF (Term Frequency–Inverse Document Frequency). It assigns a weight to each term in a document based on how often the term appears (TF) and how rare the term is across the collection (IDF). A simple ranking can be done by summing the TF-IDF weights of query terms in each document.

In practice, TF-IDF helps retrieve documents that have many occurrences of query terms, while down-weighting common terms like stopwords.

Okapi BM25: An improvement over basic TF-IDF is the BM25 ranking function. BM25 builds on the probabilistic retrieval framework and introduces mechanisms to handle term frequency saturation and document length normalization. In essence, BM25 increases a document’s score with more occurrences of a query term, but with diminishing returns, preventing overly long or repetitive documents from dominating​. . It also adjusts scores so that very long documents (which naturally have more terms) aren’t unfairly favored. BM25 is widely used in modern search engines to estimate relevance​.

For example, Elasticsearch (built on Lucene) uses BM25 as its default scoring algorithm, having replaced TF-IDF for better accuracy and performance​. This algorithm often serves as the backbone for initial retrieval stages due to its efficiency and strong baseline effectiveness.

Neural Ranking Models: In recent years, deep learning has introduced neural ranking models that go beyond bag-of-words matching. These models learn complex representations of queries and documents to capture semantic relevance. Early examples include the Deep Structured Semantic Model (DSSM), which learns vector embeddings for queries and documents and ranks by their similarity​. Neural models come in two flavors: representation-focused models (like DSSM) that encode text into dense vectors (allowing retrieval via nearest-neighbor search in embedding space), and interaction-focused models that directly learn query-document matching patterns (for example, using a transformer to attend to every query term and document term together, as BERT does).

These models can consider context and synonyms – for instance, a BERT-based ranker can understand that “NYC” and “New York City” refer to the same concept and thus appropriately score documents​. Google Search’s incorporation of BERT in 2019 is a real-world example – it helped Google better understand about 10% of English queries, improving the ranking of results for more conversational or complex searches​.

Neural ranking models have achieved state-of-the-art results on many retrieval tasks by automatically learning relevance features from data​. However, they are computationally intensive, so in practice they are often used in a re-ranking stage on a subset of candidate results rather than across an entire index.

Classification Algorithms for Ranking and Retrieval

• Support Vector Machines (SVM): SVMs are supervised learning algorithms traditionally used for classification (and regression) tasks. An SVM finds an optimal hyperplane that separates data points of different classes with the maximum margin​. In IR, SVMs have been applied to classify documents or queries (for example, classifying whether a document is relevant or not to a given query).  A notable adaptation is SVMrank, which turns ranking into a classification problem on document pairs – the SVM learns to output positive or negative labels indicating which document in a pair should be ranked higher. By doing this for many training pairs, it learns a ranking function. SVM-based rankers were among the early learning-to-rank methods and proved effective at combining features like content scores and link-based scores to improve search relevance.
• Decision Trees and Ensembles: Decision tree learning produces a model that splits data based on feature values, forming a tree where leaves represent class labels or predicted values​. A single decision tree can be used for classification (e.g., decide if a document is relevant) or regression (predict a relevance score). More powerfully, ensemble methods build multiple trees to improve performance. For instance, Random Forests combine many trees for robust classification, and Gradient Boosted Trees (like those used in LambdaMART or XGBoost) iteratively train trees to correct errors of the previous ones, directly optimizing ranking metrics. These tree-based models have been very successful in ranking tasks; they can handle many relevance features and capture nonlinear relationships.  In fact, gradient boosted decision trees were the winning approach in the Yahoo Learning-to-Rank Challenge and have been used in web search ranking at Bing and Yahoo. They essentially act as sophisticated classifiers/regressors that predict a relevance score for each query-document pair, based on input features (which may include the outputs of basic scorers like BM25, as well as other signals). Decision trees are popular in industry because they offer a good balance of interpretability, speed, and accuracy​..
• Deep Learning Classifiers: Beyond using deep networks for direct scoring (as in neural rankers above), deep learning is also used for classification tasks that support IR. Neural networks (MLPs, CNNs, RNNs, transformers) can be trained to classify text or user-item interactions. For example, a deep classifier might predict the probability that a user will click on or purchase an item (a form of binary classification used in recommendation). In natural language processing, classifiers built on transformers can determine query intent (e.g., is the query asking a question, looking for local results, or seeking an image?), which a search engine can use to route the query to the appropriate vertical or adjust the ranking. Deep classifiers have achieved high accuracy in tasks like document categorization, sentiment analysis, and spam detection – all of which can improve retrieval quality when integrated. In the context of ranking, a neural network can be trained in a pointwise fashion to output a relevance score (or a probability of the document being relevant), effectively serving as a learned scoring function.

Scorers vs. Classifiers in Learning-to-Rank (LTR)

Learning-to-Rank is a framework that explicitly combines the strengths of scorers and classifiers to optimize ranking. In LTR, we construct a model (usually via supervised machine learning) that produces an ordering of documents for a query, trained on examples of ideal rankings​. Here’s how scorers and classifiers play their roles:

• Feature Generation (Scorers as Features): First, various scoring algorithms are used to generate features for each query-document pair. For example, one feature might be the BM25 score of the document for the query, another might be the document’s click-through rate or PageRank, etc. These scoring features capture different relevance signals (textual relevance, link authority, user behavior, etc.). The quality and diversity of these features are critical – it’s common to include dozens or hundreds of features, including outputs from multiple scorers (TF-IDF, BM25, semantic similarity scores) and other content or context descriptors. In essence, before learning-to-rank can occur, the system uses “scorers” in a broad sense to quantify various aspects of relevance.

• Learning a Ranking Model (Classifiers optimizing rankings): The heart of LTR is using a machine learning model to combine those features into a final ranking score. This model acts like a classifier or regressor that predicts how relevant a document is to a query, based on the feature inputs. Depending on the approach, the learning algorithm may treat this as a pointwise problem (predict an absolute relevance score for each query-doc pair), a pairwise problem (predict which of two documents should be ranked higher), or a listwise problem (optimize the ordering of a whole list)​.

   In a pointwise approach, the model (e.g., a regression or classification model) outputs a relevance score or class (such as “relevant” vs “not relevant”) for each item independently​.

  .In a pairwise approach, the model (often an adaptation of a classifier like an SVM) takes two documents at a time and learns to output which one is better​.

  For example, RankSVM transforms ranking into a series of binary classifications on document pairs, effectively learning a decision boundary that separates “document A is more relevant than document B” cases. In a listwise approach, the model tries to optimize an entire ranked list’s quality (using losses that approximate metrics like NDCG); techniques like LambdaMART (which uses gradient boosted decision trees) directly adjust tree splits to improve the overall ranking of all documents for a query set. Regardless of approach, the learned model is typically a complex classifier/regressor that considers all input features and outputs a single relevance score used for the final sort order​..

• Contribution to Ranking Optimization: Scorers contribute by ensuring the model has informative signals to start with – if a document doesn’t have any overlapping terms with the query, a term-frequency based scorer will give it a low feature value, and the learned model can recognize it’s likely not relevant. Classifiers contribute by learning the optimal weights and non-linear combinations of these signals. For example, the model might learn that for certain query types, the “title BM25 score” is extremely important, while for other queries, the “document freshness” or a “category match” feature matters more. The ML model can thus adjust the ranking in ways a single static formula cannot, effectively learning how to rank better from training data. In a real-world case study, an analysis of a learned ranking model (LambdaMART on a web search dataset) found that classic IR scorer features like BM25 were among the most important, but the model significantly improved performance by also leveraging dozens of other features and tuning their weights​.

  .The scorer (BM25) provided a strong baseline indication of relevance​ and the classifier (LambdaMART) optimized the final ranking, for instance by overriding pure BM25 order when other signals (like click-through rates or authority scores) suggested a different ordering would better satisfy the user.

In summary, LTR marries scorers and classifiers: scorers generate candidate rankings and quantitative features, and the classifier (ranking model) learns how to best combine those to match the desired outcome. The result is a system that is far more adaptive and accurate than any single hand-crafted scoring formula, because it can learn from data how to rank. Scorers ensure no potentially relevant document is overlooked and provide the building blocks (features), while the classifier orchestrates these pieces to optimize a ranking metric (such as NDCG or precision). This division of labor is why virtually all modern search engines and many recommender systems employ LTR in some form – it provides a systematic way to improve ranking performance using machine learning.

Hybrid Systems Integrating Scoring and Classification

Real-world IR systems often implement a hybrid architecture that leverages both traditional scoring methods and modern machine learning classifiers in a complementary fashion. The general pattern is a multi-stage pipeline, as illustrated below, where an initial retrieval stage (using efficient scoring techniques) is followed by one or more reranking or filtering stages (using ML models)​.

Two-Stage Retrieval and Ranking: A common hybrid approach is a two-stage search system. In the first stage, a fast algorithm (like BM25 or a vector space cosine similarity) scans the index and pulls a set of top N candidates. This stage, sometimes called “level-0 ranking” or candidate generation, ensures recall of likely relevant items with minimal computation​.. In the second stage, a more expensive but more accurate model re-evaluates those candidates to produce the final ranked list​.

The second stage is often powered by a classifier or an LTR model that considers many features. This design is used in web search (e.g., Bing or Elasticsearch: BM25 for initial retrieval, then an LTR model reranks​) as well as in recommendations (e.g., YouTube: candidate generation network then ranking network​). The rationale is efficiency: it’s impractical to run a complex neural network or evaluate an extensive feature set over millions of documents or products per query. The initial scorer acts as a high-recall, moderate-precision filter, and the classifier-based reranker provides the high precision on a small subset.

Combining Lexical and Semantic Matching: Hybrid systems also integrate different types of scoring to cover complementary relevance signals. For instance, a search engine might use a keyword-based scorer (inverted index with BM25) alongside a neural embedding scorer. The system can retrieve some candidates via keyword matching and others via semantic similarity of embeddings, then merge and rerank them. This ensures that results include not only exact query term matches but also conceptually related content. Microsoft Bing’s large-scale search does this: it blends traditional inverted index matches with results from an embedding-based index​. By doing so, Bing can return relevant pages that don’t explicitly contain the query words, handling synonyms or topic matches, while still rewarding exact matches when they are present​.

The final ranking might be achieved by a classifier model that weighs evidence from both sources (e.g., a document that scores moderately on keyword match but very high on semantic match might be ranked above one that scores high on keywords but low semantically). Such hybrid retrieval is increasingly common in e-commerce search as well – for example, an e-commerce site might combine a text search engine’s results with recommendations from a collaborative filter if the query is vague.

Enriching Scoring with Classification Outputs: Another form of integration is using classification results as inputs to ranking. For example, a news recommendation system might first classify articles by topic or political leaning and then use those classes to ensure a user’s feed is balanced or catered to their known preferences. In web search, query classification (determining if a query is local, adult, Q&A, etc.) is often done with machine learning, and the outcome steers the scoring – e.g., a classified “local intent” query will cause the system to boost documents that are geographically relevant, or invoke a maps scorer. Likewise, classifying user segments or item categories can allow a system to adjust scores (a user classified as a “frequent gamer” might get video-game content recommendations boosted). These hybrid strategies marry content scoring with context-aware classification to improve relevance in ways a one-size-fits-all formula cannot.

Industry Examples: Many industry systems explicitly mention hybrid ranking architectures. Google’s search results, for instance, are believed to come from a layered system where an initial retrieval (using various indexes and scoring methods) is reranked by several ML models (one of which was RankBrain, an ANN-based ranker, and later neural models) – effectively a hybrid of manual scoring signals and learned models.

Netflix’s recommendation pipeline famously combines collaborative filtering (a scoring approach using past user ratings) with content-based methods (which can be seen as classifying items by genre, director, etc., and matching those to user profiles). The winning solution to the Netflix Prize was an ensemble (hybrid) of dozens of algorithms, blending matrix factorization scores with neighbor-based scores and some content features.

In e-commerce, Amazon uses a hybrid of query-document relevance scoring (to match query keywords to product text) and ML models that incorporate user behavior data (purchases, clicks) to rank products. The Amazon search team has spoken about using semantic embeddings to improve recall, combined with a learned ranking model to personalize results – a clear hybrid of scoring and classification techniques. Another vivid example is IBM Watson’s DeepQA for question answering: it did a primary search using information retrieval scorers, then a secondary answer ranking using a classifier that considered many evidence scores​. This two-pass approach improved accuracy significantly over relying on a single method alone.

Benefits of Hybrid Systems: By integrating both scoring and classification, hybrid systems achieve both breadth and depth. The scoring components ensure the system casts a wide net and efficiently handles large volumes (critical for speed and coverage), while the classification/learning components ensure nuanced factors are accounted for in ranking (critical for precision and user satisfaction). Hybrid systems also add robustness – if the learned model fails to recognize a certain relevance signal, a fallback scorer might still bring forth that result, and vice versa. Overall, the combination of static scoring algorithms with adaptive classifiers is a cornerstone of modern information retrieval, enabling search engines and recommenders to continuously learn from data while still leveraging proven retrieval techniques. The structured pipeline (retrieve then rerank, combine multiple methods) is now standard in industry because it yields significantly better search and recommendation results than any single technique in isolation.

Quality Classification and Relevance Scoring in Information Retrieval

In modern Information Retrieval (IR) and Ranking Systems, two critical aspects ensure high-quality search and recommendation results:

1. Quality Classification – determining if a document, webpage, or result meets quality standards (e.g., spam detection, credibility assessment, or content classification).
2. Relevance Scoring – measuring how well a document matches a given query and ranking it accordingly.

These two concepts often work together in search engines, recommendation systems, and AI-driven retrieval pipelines.

Quality Classification: Assessing the Credibility of Information

Quality classification is a machine learning-based classification process that ensures results are not only relevant but also trustworthy, accurate, and useful.

What is Quality Classification?

Quality classification determines whether a document is high-quality or low-quality based on predefined criteria. This process is particularly important in:

• Search engines: Filtering spam, duplicate content, or misleading pages.
• Recommendation systems: Identifying fake reviews, detecting low-quality products.
• NLP-based retrieval: Ensuring that AI-generated answers are factually correct.
• Social media and news platforms: Identifying misinformation or low-trust sources.

Common Approaches to Quality Classification

Quality classification uses machine learning models trained on labeled datasets to predict whether a document meets quality standards. Some common models include:

• Logistic Regression / Naïve Bayes: Simple classifiers for spam filtering.
• Decision Trees & Random Forests: Identify patterns based on multiple quality indicators.
• Support Vector Machines (SVMs): Distinguish between high-quality and low-quality content.
• Neural Networks (BERT, GPT-based models): Learn contextual quality indicators, such as coherence and credibility.

Features Used in Quality Classification

Quality classification models rely on multiple features, including:

• Content-based features:
  • Readability (e.g., Flesch-Kincaid score)
  • Grammar and spelling errors
  • Duplicate content detection
  • Spam indicators (e.g., excessive keyword stuffing)
• Source credibility:
  • Domain authority (e.g., Wikipedia vs. a random blog)
  • Historical trustworthiness
  • Link analysis (e.g., PageRank, spam links)
• User engagement signals:
  • Click-through rate (CTR)
  • Bounce rate (users immediately leaving)
  • Dwell time (how long users stay on a page)
• External validation:
  • User ratings and reviews
  • Social media credibility signals

Real-World Applications of Quality Classification

• Google’s Search Quality Evaluator Guidelines: Google classifies pages as High-Quality or Low-Quality based on experience, expertise, authoritativeness, and trustworthiness (E-E-A-T).
• Bing’s Spam Detection System: Uses classifiers to detect link farms, scraped content, and cloaking techniques.
• Amazon’s Fake Review Detection: Classifies reviews into genuine or fake, helping users make informed purchase decisions.
• News Verification (Fact-Checking AI): Google Fact Check Tools and Facebook AI classify articles as misinformation, biased, or verified news.

Relevance Scoring: Ranking Content Based on User Intent

While quality classification filters low-quality results, relevance scoring ensures the most useful results appear at the top.

What is Relevance Scoring?

Relevance scoring assigns a numerical value to documents based on how well they match a given query. It is a fundamental component of search engines, recommendation engines, and conversational AI systems.

Methods for Relevance Scoring

There are three main types of relevance scoring models:

A. Traditional IR Models (Lexical-Based)

• • TF-IDF (Term Frequency-Inverse Document Frequency): Scores documents based on term importance.
  • BM25: The most widely used scoring function in modern search engines.
  • Vector Space Models (VSMs): Compute cosine similarity between query and document term vectors.

📌 Example: If a user searches for “best laptops under $1000,” a document with “best laptops” mentioned several times will have a high TF-IDF/BM25 score.

B. Learning-to-Rank (LTR) Models

LTR models use machine learning to combine multiple relevance signals:

• • Pointwise models: Predict a numerical score for each document.
  • Pairwise models: Learn whether document A is more relevant than document B.
  • Listwise models: Optimize the ranking of an entire set of documents.

📌 Example: Bing and Yandex use LambdaMART, a gradient-boosted decision tree model, to optimize web search rankings.

C. Deep Learning-Based Models (Semantic Matching)

• • BERT Rankers: Understand query-document relationships beyond keywords.
  • Siamese Neural Networks: Compute sentence embeddings for relevance matching.
  • Dense Retrieval (DPR, ColBERT): Map queries/documents into vector spaces.

📌 Example: Google uses BERT-based rankers to improve search quality for long, natural-language queries.

How Quality Classification and Relevance Scoring Work Together

In most modern retrieval systems, quality classification and relevance scoring are integrated to improve search and recommendation quality.

Multi-Stage Ranking Process

Most large-scale ranking systems follow a multi-stage approach:

1. First-Stage Retrieval (Recall-Oriented)

   • Retrieves a broad set of candidate results using BM25, TF-IDF, or embedding-based retrieval.
   • This stage prioritizes recall (ensuring potentially relevant results are not missed).

2. Second-Stage Reranking (Precision-Oriented)

   • A classifier removes low-quality results (e.g., spam, low-trust content).
   • A machine learning ranker optimizes ordering using multiple signals.

3. Final Adjustments (Personalization & Business Logic)

   • Factors like user preferences, location, click behavior, and credibility are considered.
   • The final list is displayed to the user.

Industry Examples

• Google Search:

  • Uses BM25 for initial recall.
  • Applies quality classifiers to remove spam or low-authority content.
  • Uses LTR models (LambdaMART, BERT-rankers) for final ranking.

• YouTube Recommendations:

  • Candidate videos are retrieved based on viewing history and collaborative filtering.
  • A classifier filters out low-quality or misleading content.
  • A deep learning model ranks videos based on watch probability and engagement.

• E-Commerce (Amazon, eBay)

  • Product rankings combine text relevance (BM25) and user behavior (click-through rates, reviews).
  • A classifier removes fake listings, counterfeit products, or misleading descriptions.
  • A personalization model boosts items based on purchase history.

Comparison of Quality Classification vs. Relevance Scoring in Information Retrieval

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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, LLMO, AI- and 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, SERP Conf., CMCx, OMT, OMX, Campixx...

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