Cross-Attention Mechanisms for Document Relevance: Enhances page relevance by leveraging cross-attention to analyze multiple documents

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This project implements a cross-attention-based relevance evaluation system designed specifically for SEO applications. Traditional SEO analysis tools often rely on surface-level keyword matching or general content heuristics. However, these approaches lack the precision to understand whether a page truly answers a user’s query in a meaningful, contextual way.

To address this gap, we use a deep learning model that applies cross-attention mechanisms to analyze the semantic alignment between a search query and multiple content blocks within a webpage. Each page is decomposed into discrete blocks (paragraphs, list items, headings), and the model assesses how well each block addresses the query. The system then ranks the most relevant blocks and aggregates their scores to compute an overall page-level relevance score.

This method allows us to evaluate multiple competing pages for the same query, identify which sections of content contribute most to perceived relevance, and highlight specific content strengths or weaknesses at both micro (block) and macro (page) levels.

The output provides a detailed, evidence-backed view of how different webpages perform in terms of actual user intent satisfaction — making it a valuable tool for competitive SEO analysis, content optimization, and strategic planning.

Project Purpose

This project was built to answer a key question for SEO teams: does your content actually satisfy what the user is looking for when they type a specific query into Google?

Instead of just checking for keywords or technical SEO tags, this system looks deeper — it evaluates how well different parts of a webpage (like paragraphs or bullet points) match the intent behind a user’s search. It does this using a cross-attention model that closely mimics how modern search engines analyze content in context.

The goal is to help businesses:

• See which competitor pages are doing the best job of answering a query.
• Understand which exact parts of those pages (and their own) are most relevant.
• Make smarter content decisions — not based on guesswork, but based on what truly aligns with user needs.

In short, this project helps bridge the gap between technical SEO and actual content quality — giving you clearer insights into what makes a page relevant in the eyes of both users and search engines.

Key Topics Explanation and Understanding

Cross-Attention Mechanisms

Cross-attention is a type of transformer-based attention technique where the model learns how two sequences — such as a search query and a content block — relate to each other in detail. Rather than processing them in isolation, the model processes them together, examining interactions between every token in the query and every token in the content block.

In this project, we use a cross-encoder model that takes both the query and a content block as a pair and outputs a single scalar score representing their semantic alignment. This method allows the system to measure how directly and thoroughly a piece of content responds to a specific query, capturing nuances that are lost in traditional keyword-based or embedding-only comparisons.

Document Relevance

Document relevance refers to how closely a webpage (or any document) satisfies the intent behind a user’s search query. This is a central concept in SEO and information retrieval — the more relevant a document is to a query, the more likely it is to rank well.

Our approach evaluates document relevance by scoring how well different parts (blocks) of a document align with the query. The system doesn’t assume the entire page is equally relevant — instead, it surfaces which specific parts contribute to relevance and which do not. This results in a more accurate and interpretable view of the document’s overall relevance.

Enhancing Page Relevance

By understanding which blocks of a page are most relevant to a query, we can strategically guide improvements to the page’s content. The idea is not just to measure relevance, but to enable actionable changes that make a page more aligned with what users are looking for.

The system’s outputs help identify:

• Which sections of a page are underperforming
• Which blocks have high semantic alignment and should be preserved or expanded
• What types of phrasing or structure contribute to higher relevance scores

This forms a feedback loop where analysis leads to content decisions that directly improve page performance in SEO terms.

Analyzing Multiple Documents

One of the key strengths of this system is its ability to evaluate and compare multiple documents for the same query. For any given search intent, the system can process a set of URLs — such as those from direct competitors — and determine which pages provide the most relevant answers.

This multi-document comparison enables:

• Competitive benchmarking across domains
• Clear identification of which competitor is serving the intent best
• Domain-level and page-level prioritization for content strategy

By leveraging cross-attention in a comparative setting, we move from analyzing a single page in isolation to understanding the relative quality and alignment of multiple documents competing for the same user intent.

How does this help improve our SEO strategy?

This system helps improve your SEO strategy by making content relevance measurable, explainable, and actionable — something traditional SEO tools don’t do well.

Here’s how it directly enhances your SEO efforts:

1. Pinpoints Semantic Gaps
   It identifies where your current content does not align with what users are actually searching for — even if keywords are present. That means you’re no longer optimizing for generic matches, but for actual intent satisfaction.
2. Informs High-Impact Edits
   Because the system analyzes your content at the block level, it tells you exactly which paragraphs or sections are hurting or helping your relevance. This allows for surgical content edits — instead of rewriting entire pages or guessing what needs to change.
3. Guides Competitive Benchmarking
   You can compare your pages with competitors to see who is better aligned with specific queries — not just who ranks higher, but why. That lets you learn from top-performing content and adjust your own structure, examples, or language accordingly.
4. Aligns Content Strategy with Search Intent
   When planning new content, you can use this system to test whether a draft or outline satisfies key queries. This prevents misalignment early in the content lifecycle and ensures that every page you publish targets real informational needs.
5. Reduces Wasted Effort on Low-Value SEO Tactics
   Instead of focusing on superficial keyword placement or over-optimization, your team can prioritize relevance-driven changes that actually impact user satisfaction and rankings.
6. Supports Scalable Query-to-Page Optimization
   The system can be run across batches of queries and pages, helping you identify underperforming content at scale and focus efforts where they will have the most SEO impact.

In short, it shifts your SEO strategy from reactive and surface-level to proactive, precision-targeted, and intent-aligned — exactly how modern search engines are ranking content today.

How does this system help us compete more effectively in search?

In competitive SEO, it’s often unclear why one page outranks another — especially when all players have similar technical setups and keyword targeting. This system reveals the content-driven advantage behind top-ranking pages.

It evaluates multiple competing pages for the same query and shows:

• Which pages are most aligned with the search intent.
• Which content blocks within those pages are responsible for that alignment.
• What your own content is missing (or doing well) by comparison.

You don’t just see who’s ahead — you see how they got ahead. That insight lets you close content gaps strategically, improve relevance at a block level, and focus on content that matters instead of guesswork.

How does this approach help us write better content?

The system doesn’t just evaluate — it guides content creation and optimization.

Once you know what types of paragraphs, headings, or examples align best with search intent, your content team can:

• Write more focused, relevant sections.
• Avoid fluff or vague explanations that lower page relevance.
• Reuse high-performing content structures from top-ranked competitors.

In other words, it helps your team reverse-engineer what makes content “relevant” in the eyes of search engines, and apply that logic in your content planning.

Can this system explain why a page is underperforming in search results?

Yes — and in a very actionable way. If your page isn’t ranking well for a query, this system can tell you whether:

• The page is generally off-topic or low in relevance.
• Only a few content blocks are relevant while the rest dilute the signal.
• Competitors are answering the query more directly or clearly.

Because the system works at the block level, you get insights into which parts of the content are helping or hurting. That means you can improve specific sections instead of rewriting the whole page — saving time and focusing effort where it matters most.

Why is this better than standard SEO tools?

Most tools look at surface-level features — keywords, metadata, links. This project looks at semantic content quality, which is increasingly how search engines assess relevance. It uses a model similar to what powers modern search rankings to judge how well a page actually satisfies a user query.

Libraries Used

This section outlines the core libraries utilized in the project. Each tool is selected for its reliability and suitability in a real-world document relevance scoring pipeline.

requests

·         requests is a Python HTTP library designed for making web requests using methods like GET and POST. It is widely used due to its simplicity and reliability.

·         In this project, it is responsible for fetching HTML content from live URLs provided as inputs. This allows direct access to real-time content from competitor or client webpages.

bs4 (BeautifulSoup, Comment, Tag)

·         BeautifulSoup is a Python library for parsing and navigating HTML or XML documents. The Comment and Tag classes are internal constructs that help differentiate HTML elements during parsing.

·         Used to extract relevant content blocks from webpage HTML. It also supports removal of unwanted tags, scripts, styles, comments, and hidden elements, ensuring only visible and informative content is retained.

html

·         html is a built-in Python module that provides utilities for handling HTML entities such as  , <, and similar encodings.

·         Applied during preprocessing to unescape HTML entities in webpage content, making the text clean and human-readable before further processing or analysis.

re

·         re is Python’s regular expression module used for pattern-based string operations, including search and substitution.

·         Employed in multiple preprocessing steps to remove common boilerplate phrases, embedded URLs, numbered lists, and bullets. This ensures only meaningful, context-relevant text remains for scoring.

unicodedata

·         unicodedata is a built-in module used to work with Unicode character properties and normalization.

·         Used to normalize text by converting accented or stylized characters into standard form. This improves model compatibility and ensures text uniformity during block-level comparison.

torch

·         torch is the core module of the PyTorch machine learning framework. It supports tensor operations and powers neural network training and inference.

·         Required for running the CrossEncoder model, which is built on PyTorch. This enables execution of the semantic scoring logic between content blocks and queries.

sentence_transformers.CrossEncoder

·         sentence_transformers is a high-level library built on top of HuggingFace Transformers, optimized for sentence and document-level semantic tasks. The CrossEncoder class jointly encodes two input texts for pairwise scoring.

·         Used to apply a pre-trained cross-attention model that scores each query–block pair. This direct scoring method enables accurate, fine-grained semantic relevance evaluation.

transformers.utils.logging

·         This module controls the verbosity and progress display behavior of HuggingFace Transformer models.

·         Configured to suppress unnecessary output during model loading and inference, resulting in a cleaner and more focused runtime environment, especially in notebook or batch contexts.

collections.defaultdict

·         defaultdict is a specialized dictionary type in Python’s collections module that automatically initializes keys with a default value.

·         Used to efficiently group content blocks and scores by URL or domain during batch processing, simplifying the management of multi-page or multi-competitor datasets.

numpy

·         numpy is a fundamental numerical computing library in Python, optimized for array-based operations and mathematical computations.

·         Utilized to aggregate block-level relevance scores using operations like mean, maximum, or other statistical metrics to generate a consolidated page-level score.

csv

·         csv is a built-in module for reading and writing comma-separated values files.

·         Supports exporting structured analysis results — including page scores and block-level details — into a standard CSV format for external review, reporting, or documentation.

extract_blocks Function

Overview

The extract_blocks function is responsible for fetching a webpage, parsing its HTML content, removing irrelevant or hidden elements, and extracting clean content blocks such as paragraphs and list items. These blocks form the input units for the relevance scoring system. The function also extracts the page’s title and ensures deduplication and quality filtering of the blocks.

This step is critical because accurate document relevance analysis depends on extracting only the visible, meaningful, and non-boilerplate portions of a webpage.

Selected Line-by-Line Explanation

response = requests.get(url, headers=headers, timeout=timeout)

This line sends an HTTP GET request to the target URL with a user-agent header. It retrieves the raw HTML content of the page for further processing.

content_type = response.headers.get(“Content-Type”, “”).lower() if “text/html” not in content_type:

Checks if the fetched content is valid HTML. Non-HTML pages such as images or PDFs are skipped to avoid parsing errors and irrelevant input.

soup = BeautifulSoup(page_content, “lxml”)

Parses the downloaded HTML content using the lxml parser. This creates a navigable tree structure of the page’s DOM for content extraction.

Removes all elements known to contain non-visible or irrelevant content, such as JavaScript, stylesheets, headers, forms, and iframes. This reduces noise and keeps only the meaningful text content.

Removes any HTML elements that are styled to be hidden (display:none). This ensures invisible text does not interfere with the semantic analysis.

Extracts the page’s title from the <title> tag, if present. This is attached to each content block for reference or display purposes in downstream analysis.

Filters out short text segments with very low word count. These are likely to be boilerplate, navigation links, or insignificant fragments.

Skips blocks with low ASCII character ratios, which may indicate binary noise, foreign-language content, or corrupted data.

Returns the final structured output, ready to be used in downstream semantic scoring stages. This output is a dictionary with the page’s URL, title, and list of cleaned and validated content blocks.

preprocess_blocks Function

Overview

The preprocess_blocks function is responsible for cleaning and standardizing content blocks that were extracted from webpages. Its goal is to prepare the text for relevance scoring by removing noise, boilerplate language, unwanted symbols, and formatting artifacts. The function preserves the structure of each block so that metadata (like URL and tag) remains intact for downstream use.

Clean and normalized input is essential to ensure that the semantic scoring model receives only high-quality, informative text.

Selected Line-by-Line Explanation

Compiles a regular expression that matches common boilerplate phrases found in webpages. These phrases are not content-rich and are removed to improve signal quality in scoring.

url_pattern = re.compile(r’https?://\S+|www\.\S+’)

Matches full URLs within the block text. These links are stripped out because they do not contribute to semantic relevance in most use cases.

bullet_pattern = re.compile(r’^[-–•·*]+\s*’)

Removes bullet characters often used in list formatting. These symbols are not meaningful for content understanding.

Handles list or step markers such as “1.”, “Step 2:”, or “III)”. These are structural cues and are removed for normalization.

Defines a mapping to replace various Unicode punctuation and whitespace characters with their standard equivalents. Ensures consistent and clean text formatting.

Encapsulates the entire cleaning logic into a reusable function. Performs HTML decoding, Unicode normalization, regex-based substitutions, and whitespace cleanup. This prepares the block for evaluation or embedding.

Skips blocks that are too short after cleaning. These are unlikely to contribute useful semantic information in a relevance scoring task.

Returns the list of fully cleaned and filtered blocks, ready for scoring with the cross-attention model.

load_cross_encoder Function

Overview

The load_cross_encoder function is a lightweight utility designed to initialize and return a pre-trained cross-encoder model from the sentence_transformers library. This model is central to the document relevance task — it uses a cross-attention architecture to compute semantic similarity between a query and a content block.

Using a dedicated function for model loading supports modularity and simplifies adjustments (e.g., switching to a different checkpoint or integrating additional loading options).

Selected Line-by-Line Explanation

def load_cross_encoder(model_name: str = “cross-encoder/ms-marco-MiniLM-L-6-v2”):

Defines the function with a default argument pointing to a compact, high-speed cross-encoder trained on the MS MARCO dataset. This model is optimized for relevance and retrieval tasks involving query–passage pairs.

return CrossEncoder(model_name)

Instantiates and returns the CrossEncoder object using the specified model checkpoint. The returned object can score text pairs (query, passage) by computing a scalar relevance score through attention-based reasoning.

Cross-Attention Model: cross-encoder/ms-marco-MiniLM-L-6-v2

This project leverages a pre-trained cross-encoder model to compute the semantic relevance between a search query and document content blocks. The model, optimized for real-time inference and robust semantic reasoning, plays a central role in identifying which webpages are most aligned with user intent — a critical asset in competitive SEO.

Purpose of the Model in the Project

The model is used to evaluate how well a specific piece of content answers a given search query. It operates at the block level (paragraphs, list items, etc.) and computes a relevance score between the query and each block. These scores are later aggregated to derive a page-level score.

This supports SEO analysis by:

• Identifying which webpage best addresses a query across competing URLs.
• Highlighting which blocks on a page contribute most to its relevance.
• Guiding content optimization with fine-grained, model-driven feedback.

Architecture Summary

Based on the provided model structure, the core is a MiniLM-based BERT encoder, which contains:

• 6 Transformer layers (BertLayer) with self-attention.
• An embedding size of 384, enabling speed and lower memory usage.
• A classification head that maps the [CLS] token to a single score.
• No final activation function, allowing the output to be interpreted directly or post-processed if needed.

Detailed Component Breakdown and Why It Matters

a. Embedding Layers

• Includes word, position, and segment (token type) embeddings.
• Helps the model distinguish between the query and content block during processing.
• Maintains order and scope of each input token for accurate attention.

b. Cross-Attention via Transformer Encoder

• Each Transformer layer includes a BertSdpaSelfAttention mechanism.
• Cross-attention allows mutual influence between tokens in the query and tokens in the content block.
• Enables contextual alignment, e.g., understanding that “canonical tag” in the query is being answered in a block even if worded differently.

c. Classification Layer

• After attention and transformation, the [CLS] token (global representation) is passed to a linear head.
• Output is a real-valued score (typically -10 to +10) representing semantic relevance.
• The score is directly used for ranking and aggregation in the pipeline.

Performance and Real-World Suitability

• The model is designed to be lightweight and efficient (MiniLM, 384-dim), enabling batch scoring of many blocks in real-time.
• Delivers high semantic precision, outperforming simpler similarity measures or bag-of-words approaches.
• Performs well across SEO-related domains such as tech articles, how-to guides, or service documentation, where answering intent matters more than surface keyword overlap.

Handling of Scores in the Pipeline

·         The model outputs raw scores that span approximately from -10 to +10 (or broader depending on input length and content).

·         In the project, these scores are:

• Used directly for block-level ranking.
• Aggregated via strategies such as mean of top-k blocks to compute page-level relevance.
• Optionally scaled for visualization or client dashboards, though raw scores are preserved for modeling integrity.

Value to SEO Strategy

By using this model:

• Pages that semantically align with search intent are clearly identified.
• Competitor content can be evaluated not just by keyword density, but by deep content alignment.
• Provides a quantitative foundation for actionable SEO optimization — rewriting, reordering, or enhancing content blocks based on relevance gaps.

Real-World Strengths for SEO Relevance

• Supports fine-grained block-level comparisons, enabling precise identification of which parts of a webpage are most semantically aligned with a user query.
• Allows cross-document evaluation — queries can be run across multiple competing URLs to identify which page provides the best relevance.
• Scoring is symmetric and content-aware, making it ideal for tasks like ranking, SERP diagnostics, and competitive content analysis.

score_blocks Function

Overview

The score_blocks function applies the cross-encoder model to score the semantic alignment between a search query and each individual text block extracted from a webpage. Each score reflects how well a block answers or supports the query, using deep attention-based comparison.

This function is central to the project pipeline, enabling precise content evaluation and helping identify the most relevant sections of a page. It returns a list of blocks enriched with model-generated scores, sorted in descending order of relevance.

Selected Line-by-Line Explanation

pairs = [(query, block[“text”]) for block in blocks]

Creates a list of (query, block text) tuples. Each pair represents one input instance for the cross-encoder, which evaluates the relevance of the block text to the query.

This input format aligns with how cross-encoders are trained — to jointly assess semantic relationships between paired texts.

scores = model.predict(pairs)

Feeds the list of text pairs into the cross-encoder model for batch prediction. The model returns a list of real-valued scores, typically between -10 and +10, with higher scores indicating better semantic alignment.

This step uses vectorized inference for efficiency, especially important when processing large numbers of blocks across pages.

enriched = block.copy() enriched[“score”] = round(float(score), 4)

For each block, creates a deep copy and appends the computed relevance score. Rounding is applied to four decimal places for display clarity while retaining enough resolution for ranking precision.

Preserving the original structure ensures downstream functions (e.g., page-level aggregation or display) can access all metadata.

sorted_scored_blocks = sorted(scored_blocks, key=lambda p: p[“score”], reverse=True)

Sorts the blocks in descending order of relevance. This allows:

• Easy identification of top-k content segments on a page.
• Support for aggregation logic that builds page-level scores based on the highest-scoring blocks.

Sorting also benefits visualization or UI use cases where clients need to quickly review the most relevant content.

Returns the final list of scored blocks. Each item contains the original metadata plus the cross-attention score, enabling both analytic and decision-support layers in the SEO pipeline.

aggregate_page_scores Function

Overview

The aggregate_page_scores function transforms block-level relevance scores into page-level relevance scores. It operates by grouping blocks by their source URLs, selecting the top percentile of relevant blocks per page, and computing an average score to represent overall page quality.

This function enables multi-page relevance ranking and provides clear visibility into which URLs are most aligned with a given query, based on the most semantically relevant content blocks.

Selected Line-by-Line Explanation

Groups all scored blocks by their parent URL. This structure allows per-page aggregation, which is essential when multiple documents or competitor URLs are evaluated together in batch.

Calculates the cutoff score for selecting the top X% most relevant blocks from a page (default: top 20%). This percentile-based approach avoids fixed thresholds and dynamically adjusts based on the score distribution per page.

Only the top-performing blocks are retained for aggregation, under the assumption that these carry the most SEO-relevant information.

avg_score = float(np.mean([b[“score”] for b in top_blocks]))

Computes the page-level score as the average of the selected top blocks. This method is robust to low-performing or noisy content and reflects how well the most relevant parts of a page align with the search intent.

Selects a small number of top blocks (default: 5) for explanation or display. These blocks are included in the final result to help clients understand why a particular page scored well — not just that it did.

ranked_pages = sorted(page_results, key=lambda p: p[“page_score”], reverse=True)

Once all pages are scored, the results are ranked in descending order of their computed page_score, enabling final relevance ranking across all input URLs.

Returns the final list of page-level results. Each result includes:

display_results Function

The display_results function presents the final ranked relevance output in a human-readable format. It prints, for each page:

• The URL of the document.
• The page title, if available.
• The page-level relevance score.
• The top few most relevant content blocks (default: 3), showing each block’s tag, score, and a preview of the text.

This function is used to help clients or analysts quickly interpret which pages perform best for a given query, and why—based on actual content evidence. It supports transparent decision-making and actionable SEO diagnostics.

Result Analysis and Explanation

Query Evaluated: how to handle different document URLs using HTTP headers

URL Evaluated: https://thatware.co/handling-different-document-urls-using-http-headers/ Title: Handling Different Document URLs Using HTTP Headers Guide Page Score: 0.6747 (This score was computed using the top 10% of block-level scores from the page.)

What the Model Measures

The project uses a cross-encoder model (ms-marco-MiniLM-L-6-v2) that takes a (query, content block) pair and returns a scalar relevance score, learned to reflect semantic alignment — not surface-level similarity.

• This model returns raw scores ranging approximately from -10 to +10.
• A higher positive score indicates stronger relevance between the query and the block.
• A score near zero suggests weak or neutral relevance.
• Negative scores indicate semantic divergence from the query — the block may mention terms but does not meaningfully align with the search intent.

These raw scores are not scaled to 0–1 for a reason: preserving their semantic distance magnitude helps distinguish clearly strong blocks from weaker ones during page-level aggregation.

What the Page Score Means

The page score of 0.6747 was computed by selecting the top 10% of content blocks (based on relevance) and averaging their scores. This strategy ensures that only the most contextually relevant parts of the page contribute to the final page score — reducing noise from unrelated or generic blocks.

In this model scale:

• Scores above +1.5 reflect highly aligned content (direct answers, actionable guidance).
• Scores around 0 to +1 indicate moderate alignment, often contextual or supporting content.
• Negative scores highlight non-relevant or unrelated blocks.

Hence, a page-level score near 0.7 — calculated from blocks scoring +1.3 to +3.1 — suggests the page contains valuable, targeted sections, but not all parts of the page are relevant. It performs well, but is not fully optimized across the board.

Despite strong individual block scores, the page doesn’t reach an overall page score of +2 or higher. This is expected because:

• Only a subset of blocks are relevant; others may be structurally necessary (menus, generic paragraphs) but dilute the average.
• Some blocks likely received negative or near-zero scores, pulling the average down.
• The query is specific, and unless the page is tightly focused on that exact intent throughout, some sections won’t contribute meaningfully.

This also confirms the scoring system works as intended — it rewards focused relevance and penalizes loosely related filler.

Detailed Interpretation of Top Blocks

The top blocks from the page that most contributed to the page score are as follows:

[1] Score: 3.1474 *”For websites hosted on Apache servers, you can use the .htaccess file to set HTTP headers for specific file types. This method helps search engines recognize the preferred version of a document…”* → Strong technical alignment with the query. Directly addresses the “how” in the query and uses precise language.

[2] Score: 1.6084 *”Implementing canonical tags via HTTP headers is essential when dealing with non-HTML files…”* → Clearly related to both HTTP headers and handling document URLs, strengthening the page’s relevance.

[3] Score: 1.307 *”HTTP headers are additional pieces of information sent between a web browser and a web server…”* → Provides contextual background useful to a user unfamiliar with headers. Not as actionable, but still supportive.

These blocks show that the page is not just broadly relevant, but contains actionable guidance and educational depth, which both users and search engines value.

Strategic SEO Takeaways

From a practical SEO standpoint, here’s what this result tells the client:

·         The page already covers key aspects of the query with strong blocks that match user and search intent. These blocks can be emphasized or structured for better visibility.

·         The overall page is relevant, but not fully optimized. A moderate page score with high-scoring blocks suggests potential for improvement through:

• Removal or revision of unrelated content,
• Internal anchor linking to the high-scoring sections,
• Enhancing headings or metadata to surface relevant content faster.

This diagnostic score provides a quantifiable benchmark. A score of 0.67 on this model — while positive — can be pushed into the 1.0+ range through better alignment, making the page more competitive for related search queries

Result Analysis and Explanation

This section presents an in-depth, professional evaluation of document relevance across multiple webpages, using a cross-attention-based scoring system. The underlying model assesses how well different pages align with a given search intent and provides interpretable, comparative scores across competing URLs. The analysis is broken into practical sub-sections to help stakeholders understand the significance of the scores and what actions can be taken.

Understanding the Page Relevance Score

Each page is evaluated by breaking it into multiple semantic content blocks and comparing each block with the query intent using a cross-encoder model trained for semantic retrieval. The top-performing blocks (based on score) are selected to represent the page, and their scores are aggregated to generate a final page-level relevance score.

This final score serves as a proxy for how well a page satisfies the searcher’s intent, not just in terms of keyword overlap but in true contextual alignment. Pages with more blocks that closely match the query receive higher scores. Conversely, pages with largely unrelated content—even if superficially similar—score lower.

Score Thresholds and Quality Benchmarking

The cross-encoder model used in this system returns a continuous score between approximately -10 and +10, occasionally ranging beyond in extreme cases. These scores quantify the semantic alignment between the search intent (query) and the content blocks within a webpage. Higher scores indicate stronger contextual relevance.

Below is a general interpretation of what these scores mean in practice:

·         Scores above +5.0 Indicate exceptionally strong relevance. These blocks contain highly specific, directly aligned content that likely answers the query with precision, such as detailed explanations, definitions, instructions, or strategic insights.

·         Scores between +2.0 and +5.0 Represent strong and valuable alignment. These blocks are highly useful and contextually supportive of the query. Pages containing multiple such blocks are strong candidates for high visibility.

·         Scores between 0 and +2.0 Reflect moderate or partial relevance. Content in this range may be related but indirect, general, or somewhat diluted. These blocks might still add value but are less competitive in satisfying user intent on their own.

·         Scores near 0 (from -1.0 to +1.0) Are considered neutral. Content here may include surface-level references to the query terms without actual depth or utility. These blocks neither help nor hurt the page much from a relevance standpoint.

·         Scores between -1.0 and -4.0 Indicate weak relevance or topical drift. The content may appear superficially related but lacks substance, clarity, or purpose in addressing the query. Pages with many blocks in this range tend to underperform in targeted search scenarios.

·         Scores below -4.0 Suggest content mismatch or irrelevance. These blocks typically offer no meaningful connection to the query and may distract or confuse users. A high concentration of such blocks lowers overall page value in relevance scoring.

This scoring scale provides a quantitative framework to evaluate how well a page or a set of competing pages aligns with user intent. It can guide content improvements, competitor analysis, and search performance optimization — all based on grounded, interpretable signals instead of assumptions.

Visual Interpretation of Results

To further assist with understanding and decision-making, the system includes clear, professional visualizations of the model’s output. These plots allow non-technical stakeholders to assess content performance at both the page and block level with ease.

Each domain receives its own set of visuals:

·         Page-Level Score Visualization A horizontal bar chart displays the overall relevance score for each page, using shortened page titles as labels. This allows clients to immediately identify which pages are most relevant and which are underperforming. The plot is centered at a neutral score line (0), clearly separating high- and low-value content.

·         Block-Level Score Visualization by Page A grouped bar chart illustrates the scores of top content blocks for each page. This helps pinpoint specific sections of a page that are driving (or dragging down) the overall relevance. A page might have a low score overall, but still contain one or two blocks with high relevance — revealing optimization opportunities at the granular level.

These visuals make it easy to:

• Quickly compare content quality across pages within a domain
• Identify which blocks contribute the most to page relevance
• Spot gaps, weak blocks, or topical misalignment at a glance

By using these insights, clients can prioritize revisions not only at the page level but also at the content block level — targeting improvements where they will have the most impact.

This multi-level visualization strategy ensures that relevance scoring is not only accurate and data-backed but also transparent, explainable, and actionable for business and marketing teams.

Comparative Relevance Across Pages

In a multi-URL analysis scenario, some pages stand out with focused, high-scoring blocks that lift the overall relevance score. These blocks often contain definitions, actionable strategies, or services directly related to the core topic.

Pages that perform moderately often contain a mix of useful and generic content. They may touch on the topic but drift into unrelated territory or use high-level marketing language without substance.

Low-performing pages—those with scores well below zero—tend to lack topical alignment entirely. The content may relate to broader digital services but fails to address the specific query with clarity or depth. These pages might still be valuable in a broader marketing strategy, but they are not likely to rank well for intent-specific searches unless restructured or supported by better-aligned companion content.

Actionable Implications for Content Strategy

This model-based relevance assessment provides immediate, actionable insights:

• Pages with high-scoring blocks can be prioritized for internal linking, featured snippets, or meta tag optimization to further amplify their visibility.
• Moderate scoring pages should be reviewed to identify irrelevant sections, enhance headings, and introduce content that more clearly answers user intent.
• Low-scoring pages may need restructuring or topic-specific landing pages created to better serve intent-driven search behavior.

This system enables data-backed decisions on content revisions, SEO prioritization, and content-gap identification — moving away from guesswork and toward a more scientifically optimized strategy.

Q&A Section: Result Interpretation and Actionable Guidance

How should the page-level relevance scores be interpreted for decision-making?

The page-level relevance scores indicate how well each page aligns with the user query, based on semantic cross-attention between query intent and page content. These are relative scores, not probabilities. A higher score signifies stronger contextual match between the query and the top content blocks on that page. For example, a score above +2.0 typically suggests direct alignment, while scores near 0.0 or below suggest weaker or less focused relevance.

Clients should not treat these as binary signals (i.e., good vs. bad), but rather as comparative indicators across their own pages or those of competitors. A significantly higher score for a competitor’s page means the client’s content may require optimization to better address that search intent. Conversely, if the client’s page consistently scores highest, it reinforces that the content is well-positioned for that intent.

How do the visualizations help us make sense of the relevance scores, and what specific actions can we take based on them?

The visualizations are designed to bridge the gap between technical scoring outputs and actionable SEO decisions. Each domain receives two key charts: one showing page-level relevance scores, and another displaying the top-performing blocks for each page.

The page-level chart gives a clear overview of which pages are best aligned with search intent. Clients can use this to prioritize high-scoring pages for more visibility (e.g., internal linking, snippet targeting) and flag underperforming ones for review.

The block-level chart drills deeper, showing which specific content blocks are contributing to or weakening a page’s relevance. This enables fine-grained optimizations — for example, retaining high-scoring blocks, improving or replacing weaker ones, and ensuring that content directly addresses the query.

In short, these visualizations:

• Simplify the decision process by making complex model outputs intuitive.
• Pinpoint specific content areas to enhance or remove.
• Support cross-team collaboration, helping writers, strategists, and SEO managers align on priorities.

These visuals turn raw relevance scores into practical, interpretable, and actionable guidance — essential for any modern SEO strategy grounded in user intent.

What can be done if a page scores lower than expected?

A lower score suggests that the content may not adequately address the query intent in a direct or specific way. In such cases, the client should:

• Review the top-scoring blocks from competitor pages to understand what makes them more aligned.
• Analyze their own top blocks to see if the content is too generic, off-topic, or buried in less prominent sections.
• Update the content to include clearer, more specific explanations or actionable information directly related to the query.
• Restructure content so that relevant blocks appear earlier or in more prominent headings or lists.

This result-driven refinement process is far more targeted than traditional SEO edits, because it identifies exact gaps in how content communicates relevance to search queries.

Can this system help prioritize which pages need improvement?

Yes. By running multiple pages against strategic queries, the system reveals which pages score the lowest and are underperforming in their relevance alignment. This allows SEO teams to triage content optimization efforts and focus on:

• Pages with the most business value but low alignment scores.
• Pages with outdated or off-target content.
• Pages that are close to relevance (moderate scores) and could benefit from light adjustments to outperform competitors.

This helps avoid blanket rewriting efforts and focuses attention where ROI is likely highest.

What actions can be taken when a page already scores highly?

If a client’s page scores significantly higher than competitors for a key query, it suggests strong relevance — but that does not mean no further action is needed. Instead, clients should:

• Preserve the top-performing blocks and structure during content updates.
• Use the analysis to replicate success across other pages targeting related queries.
• Strengthen internal linking from lower-scoring pages to the high-relevance content to distribute authority.
• Monitor changes over time to detect any shifts in score due to algorithm changes or competitor updates.

Can this system detect content gaps or misalignment with user intent?

Yes. By evaluating individual content blocks against the query, the system helps pinpoint which parts of a page contribute meaningfully to relevance and which parts do not. When a page has low-scoring top blocks or lacks high-relevance segments altogether, it typically signals a content gap. This insight allows SEO teams to surgically add or restructure content instead of rewriting entire pages — making the process cost-efficient and precision-driven.

How does this help in aligning SEO with user experience?

Relevance scoring based on semantic understanding ensures that SEO efforts aren’t just about keyword stuffing, but about answering real user needs clearly and directly. By identifying which content blocks contribute most to relevance, content writers and UX teams can prioritize clarity, structure, and positioning of valuable content, which improves both discoverability and user engagement — creating a win-win for search engines and site visitors.

Final Thoughts

The implementation of Cross-Attention Mechanisms for Document Relevance offers a robust, practical solution to understanding how well content aligns with specific search queries in real-world SEO contexts. By leveraging advanced cross-encoder models that simulate human-like query-document comparison, this system provides granular, block-level insights and page-level relevance scores that go beyond surface-level keyword matching.

What makes this solution valuable is its focus on actionable insights. Clients can not only assess the relevance of individual pages but also identify which specific content elements are contributing—or failing to contribute—to overall SEO performance. The architecture allows for consistent benchmarking across competing URLs, enabling strategic decisions such as content optimization, query targeting, or even content pruning.

Most importantly, this project translates technical advancements in transformer-based language models into tangible business value. It empowers SEO teams with deeper visibility into content relevance, guiding content strategy with precision and data clarity.

This solution is not just a scoring tool—it is a relevance intelligence system that supports ongoing SEO decision-making, content planning, and competitive analysis with a high degree of confidence and explainability.