Interpretability in Ranking Models: Offers explanations for why a page was ranked high, improving trust and usability

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This project aims to improve the transparency and trustworthiness of search engine ranking models by offering detailed, interpretable explanations for why a webpage ranks highly for specific search queries. Instead of treating the page as a whole, the approach analyzes individual content blocks—such as paragraphs, headings, and lists—to assess their relevance based on semantic similarity, keyword matching, and structural importance.

Each content block is scored and ranked, and accompanying factual explanations are generated to clearly articulate the block’s contribution to the page’s overall relevance. This fine-grained interpretability empowers SEO professionals and clients to pinpoint exactly which parts of the content are driving ranking performance and why. By understanding the ranking rationale, users can make targeted improvements, build trust in automated ranking systems, and enhance content strategy.

In addition to scoring and explanations, the project provides intuitive visualizations that summarize key relevance metrics at the page and query levels, making complex model outputs accessible to non-technical stakeholders. Overall, this project bridges the gap between black-box ranking algorithms and practical SEO decision-making, offering actionable insights that improve both usability and confidence in search rankings.

Project Purpose

The primary purpose of this project is to enhance the transparency and trustworthiness of SEO ranking models by providing clear, concise explanations for why a specific webpage is ranked highly for a given search query. In the SEO domain, clients and strategists often face challenges understanding the rationale behind search engine rankings, which impacts decision-making and confidence in optimization efforts.

This project addresses that gap by analyzing page content at the granular block level—examining individual sections or paragraphs—and scoring their relevance to the query. It integrates multiple scoring signals including semantic similarity, keyword presence, and heading matches to produce a comprehensive ranking explanation. The generated factual and natural-language explanations clarify how and why each content block contributes to the page’s overall rank.

By delivering interpretable insights, the project enables SEO professionals and their clients to:

• Gain actionable understanding of the content elements driving rankings
• Identify strengths and gaps within the page content relative to the query
• Make informed decisions to improve content relevance and search performance
• Build trust with clients through transparent reporting of ranking factors

In summary, this project’s goal is to bridge the interpretability gap in ranking models, providing users with practical, understandable reasons for page ranking that enhance usability and confidence in SEO strategies.

Project’s Key Topics Explanation and Understanding

Interpretability in Ranking Models

In the context of search engine optimization (SEO), ranking models determine the order in which web pages appear in search results. These models can be powered by complex algorithms, often using machine learning techniques that weigh hundreds of signals — from keyword relevance and content quality to user engagement patterns. While the outcome (the ranking position) is visible to everyone, the reasoning behind why a page ranked high often remains opaque.

Interpretability refers to the ability to explain these underlying reasons in a clear, human-readable way. For SEO strategists and content teams, interpretability bridges the gap between technical ranking mechanics and actionable insights. Without it, optimization efforts risk becoming guesswork — applying changes blindly without knowing if they address the actual ranking factors. With interpretability, each recommendation is grounded in specific, evidence-backed reasons, enabling targeted and efficient improvement strategies.

Feature Importance and Ranking Signals

Every ranking model relies on a set of features (or ranking signals) to assess a page. These could include on-page factors (such as title relevance, content depth, and keyword coverage), off-page factors (like backlinks and domain authority), and behavioral metrics (including click-through rates and dwell time). In advanced models, semantic understanding and context matching through embeddings also play a major role.

Feature importance analysis reveals which of these signals contributed most to a specific ranking decision. In client-facing terms, this means we can tell you whether your high rank is primarily driven by strong topical coverage, robust backlink profiles, exceptional engagement metrics — or perhaps a combination of factors. This kind of breakdown not only explains the current ranking but also highlights where competitive vulnerabilities may exist.

Trust and Usability in Ranking Decisions

From a client perspective, SEO reporting without explanations can feel like being given a medical test result without any interpretation. Knowing that a page ranks #3 is useful, but understanding why it ranks there transforms that information into actionable strategy. Interpretability addresses this need by showing the reasoning behind the model’s ranking.

Trust grows when the decision-making process is transparent. For example, if a model consistently rewards comprehensive content that addresses multiple aspects of a user’s query, this insight can guide your entire content production strategy. Usability improves because these explanations turn raw metrics into specific, high-priority tasks — whether that’s expanding certain sections, improving page speed, or refining keyword targeting.

Model-Agnostic and Model-Specific Approaches

There are two broad categories of interpretability techniques that are relevant here:

• Model-Agnostic Methods: These approaches can be applied to any ranking model, regardless of its internal architecture. Tools like LIME (Local Interpretable Model-Agnostic Explanations) or SHAP (SHapley Additive exPlanations) simulate small input changes to see how they affect rankings, identifying the most influential features in a human-readable format.
• Model-Specific Methods: These leverage the internal design of the ranking model. For example, transformer-based ranking models may allow us to examine attention weights to see which parts of a query or page text were most influential in relevance scoring.

For our project, we design the pipeline to be flexible — capable of working with either category — so that the interpretability framework remains robust even if the underlying ranking model changes in the future.

SEO-Specific Adaptation of Interpretability

Generic interpretability outputs are often too abstract for SEO teams. We adapt these explanations into SEO-ready narratives that translate technical ranking factors into clear business language. For instance, instead of reporting “Feature X weight = 0.28,” we might deliver:

“The model favored your page because the section covering [subtopic] matched the query’s informational intent better than competing pages.”

This adaptation is what makes the results directly usable by strategists, editors, and decision-makers without requiring them to have machine learning expertise. It also ensures that explanations naturally tie back to familiar SEO concepts — relevance, authority, technical health, and engagement.

The Bigger Picture: Strategic Decision-Making

Ultimately, interpretability isn’t just about explaining the present — it’s about informing future action. When you understand why a page ranked high, you can replicate that success across other target queries. Conversely, when a competitor outranks you, interpretability shows you the precise leverage points to reclaim that position.

This turns ranking models from passive reporting tools into active strategic partners in SEO campaigns. Over time, the ability to see not just the “score” but the “reason” creates a compounding effect on content quality, keyword targeting, and competitive positioning.

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What is the main benefit of having interpretability in ranking models?

The primary benefit is transparency. You can see why a page ranks the way it does — whether it’s due to keyword relevance, content depth, user engagement signals, or backlink authority. This transparency allows you to validate that rankings are aligned with your SEO strategy rather than relying on assumptions.

This project adds a layer of transparency to search engine rankings by revealing why specific pages appear higher in search results. Instead of relying on guesswork or generic SEO best practices, you gain page-specific explanations that identify which content elements, keyword placements, structural choices, or contextual signals are driving performance. For instance, if a product page ranks highly, the explanation might reveal that its structured data markup and clear topical alignment with the search query were key drivers. This allows you to double down on winning strategies while identifying underperforming elements on other pages. The impact is twofold: you save time by focusing only on effective optimizations, and you reduce wasted effort on tactics that don’t influence rankings. In competitive SEO niches, this insight can be the deciding factor in gaining or maintaining top positions.

What does this project reveal that traditional SEO analytics can’t?

Most SEO reports show what is ranking, but not why. This project bridges that gap by identifying the specific content features, intent matches, and contextual signals that contribute to higher rankings, enabling a deeper understanding of search engine priorities.

Why does this matter for improving website performance?

Knowing the exact ranking factors lets you replicate success across other pages. For example, if top-ranking pages share strong semantic coverage of certain subtopics, you can ensure underperforming pages include similar depth and breadth of coverage.

What advantage does this give over competitors?

Competitors without interpretability tools are guessing at what’s driving search results. With this project, you have evidence-backed explanations that inform content and optimization decisions, letting you move faster and target improvements with precision.

What is the benefit for long-term SEO strategy?

It helps you future-proof your SEO approach. By understanding the deeper patterns behind rankings, you can adapt more easily to algorithm changes because you know which underlying factors remain valuable over time.

What kind of SEO problems can this help solve?

It can uncover misalignment between page intent and ranking intent, detect over-optimization issues, highlight missing topics, and show when rankings are being driven by off-page rather than on-page factors — enabling targeted fixes.

Why is this valuable for stakeholders beyond the SEO team?

Marketing teams get insights into audience needs, content teams understand what topics resonate in rankings, and leadership gets clarity on the ROI of SEO efforts. It turns ranking analysis into a cross-team, data-driven conversation.

Libraries Used

requests

requests is a Python library for making HTTP requests in a simple and human-readable way. It handles network communications, sending GET/POST requests, and managing responses from web servers without requiring complex networking code.

Used requests to fetch the raw HTML content of the client-provided webpages. This allows our system to directly pull live content from the internet for analysis, ensuring results reflect the most up-to-date version of the page.

urllib.parse (urlparse)

Part of Python’s standard library, urlparse helps break down a URL into its individual components (like domain, path, query parameters) for easier handling.

Used urlparse to identify and separate the core domain from additional path or query details. This is useful for content mapping and ensuring that results can be tied back to the correct source page.

BeautifulSoup & Comment (from bs4)

BeautifulSoup is a Python library for parsing HTML and XML content. It allows developers to navigate, search, and modify HTML structures easily. The Comment object specifically detects and extracts HTML comments.

Used BeautifulSoup to clean and extract visible content blocks from a page, removing HTML tags, scripts, and other non-essential elements. The Comment feature ensures hidden or commented-out sections are handled appropriately so they don’t distort the analysis.

re (Regular Expressions)

The re module allows for advanced pattern matching and text searching using regular expressions. It’s a built-in Python tool for detecting and manipulating text based on specific rules.

Used regular expressions to strip unwanted elements, normalize spacing, and detect specific text patterns in the cleaned content, helping maintain high-quality input for analysis.

html

The html module provides utilities for handling HTML entities—those special codes like & for &.

Used it to decode HTML entities into their readable characters so that the extracted text is clean and human-readable before further processing.

unicodedata

The unicodedata module offers tools for working with Unicode characters—allowing normalization and standardized representation of text.

Used Unicode normalization to ensure all characters are consistently encoded, preventing issues where visually identical characters are treated differently by models.

time

A built-in Python module that handles time-related tasks such as delays, timestamps, and performance measurements.

Used time to manage request pacing, calculate page loading time, avoiding overloading web servers and complying with polite crawling practices.

transformers.pipeline

Part of the Hugging Face transformers library, the pipeline function provides a quick interface to pre-trained NLP models for tasks like classification, summarization, and sentiment analysis.

Used the pipeline to run DeBERTa-based zero-shot classification for understanding the search intent of each content section without needing custom model training.

sentence_transformers.util

This utility module from sentence-transformers includes functions for comparing and analyzing text embeddings (vectorized representations of text).

Used it to calculate similarity scores between queries and content sections, which is essential for detecting alignment or drift in intent.

torch

PyTorch is a deep learning framework that powers many NLP models, including those from Hugging Face and Sentence Transformers.

Used PyTorch as the backend for running transformer models efficiently, allowing for faster inference during intent classification and similarity scoring.

SentenceTransformer (from sentence_transformers)

SentenceTransformer is a high-level interface for generating vector embeddings of sentences and paragraphs using transformer models.

Used it to embed both queries and content sections into vector space, enabling precise semantic similarity comparisons.

transformers.utils (logging control)

This is a utility module in Hugging Face’s transformers library that provides control over logging and progress bars.

Configured logging here to suppress unnecessary output in the notebook, keeping the client-facing results clean and focused.

numpy (np)

NumPy is a core Python library for numerical computing, offering fast operations on arrays and matrices.

Used NumPy to handle vector math for similarity calculations and drift scoring in an efficient way.

pandas (pd)

Pandas is a data analysis library providing powerful data structures like DataFrames for structured data manipulation.

Used Pandas to organize query-intent alignment results and generate analysis-friendly tables that can be directly visualized or reported.

matplotlib.pyplot (plt)

Matplotlib is a Python plotting library for creating static, interactive, and animated visualizations.

Used pyplot to generate clear and interpretable charts showing alignment trends, drift levels, and section similarity distributions.

seaborn (sns)

Seaborn is a statistical data visualization library built on top of Matplotlib, known for its clean and modern chart designs.

Used Seaborn to create visually appealing, easy-to-read plots for presenting drift detection results in a client-friendly manner.

Function: validate_url(url: str) -> bool

Function Summary

The validate_url function ensures that any given webpage link is both structurally correct and reachable before proceeding with further content analysis. In practical terms, it prevents wasted computation and errors later in the pipeline by filtering out malformed or dead URLs at the very start. This is critical for maintaining pipeline reliability when handling multiple client-supplied URLs.

Key Code Explanations

1. URL Structure Validation

• Checks if the URL contains a valid scheme (e.g., http, https) and a domain (e.g., example.com).
• Many errors in scraping or API calls originate from malformed URLs such as missing https:// or lacking a domain. Catching these early avoids unnecessary HTTP requests.

2. Reachability Check

• Sends a lightweight HEAD request to the server instead of a full GET. This checks if the page exists and is responsive.
• Using HEAD avoids downloading the full content unnecessarily.
• Any HTTP status code 400 or above indicates a problem (client error, server error, etc.), so those URLs are rejected.
• Helps ensure that later NLP processes run only on accessible, live pages.

3. Exception Handling

• Catches network-related issues, timeouts, or other request errors.
• This prevents the pipeline from breaking due to transient network failures or inaccessible domains, and instead provides clear error feedback.

Function: extract_clean_blocks(url: str, min_words: int = 8, timeout: int = 10) -> dict

Function Summary

The extract_clean_blocks function retrieves, cleans, and organizes meaningful textual content from a given webpage. It removes non-essential elements like scripts, ads, and navigation bars, then extracts only visible, content-rich text blocks. The function ensures each block meets quality standards (word count, ASCII ratio, uniqueness) before returning a structured dictionary with page metadata, title, and processed content blocks. This is a foundational step for the project’s ranking interpretability pipeline, ensuring that the input to scoring models contains only relevant, high-quality textual data.

Key Code Explanations

1. Removing Non-Content Tags

• Strips away HTML elements that do not contribute meaningful textual information, such as JavaScript, stylesheets, or site navigation.
• This helps avoid polluting the model input with irrelevant text, which could lower scoring accuracy and mislead interpretability results.

2. Filtering Hidden or Commented Content

• Detects and removes HTML elements that are visually hidden (display:none) or exist only in HTML comments.
• Hidden or commented-out text may contain outdated, irrelevant, or misleading information not visible to actual users — keeping only visible content aligns with real-world user experience.

3. Cleaning Individual Text Blocks

• Removes repetitive “boilerplate” phrases like “read more” or “subscribe,” as well as embedded URLs from text blocks.
• These boilerplate items can skew keyword and similarity scores if left in, creating noise in ranking explanations.

4. Quality Filtering of Blocks

• Minimum Word Count: Ensures each block has enough words to be meaningful.
• ASCII Ratio Check: Filters out content dominated by non-Latin scripts or symbols, unless that’s relevant.
• Duplicate Detection: Prevents repeated blocks from cluttering the results.
• These quality checks help ensure the dataset is concise, unique, and language-consistent for accurate scoring.

5. Returning Structured Output

• Organizes extracted content into a clear format containing metadata (total word count, number of blocks, estimated load time) alongside cleaned text blocks.
• A structured return format ensures easy downstream use by the ranking and interpretability models without further preprocessing.

Function: load_embedding_model

Function Summary

This function is responsible for loading a pre-trained SentenceTransformer model that can generate embeddings for textual data. The model is selected from the HuggingFace model repository, and it is automatically allocated to the appropriate computation device — GPU (if available) for faster processing or CPU otherwise. This ensures flexibility and efficiency across different hardware environments.

Key Code Explanations

1.    device = “cuda” if torch.cuda.is_available() else “cpu”

• This line checks if a CUDA-enabled GPU is available on the system.
• If it is, the device is set to “cuda” for faster embedding generation.
• If not, it defaults to “cpu”, ensuring compatibility on all systems.

2.    model = SentenceTransformer(model_name, device=device)

• Loads the SentenceTransformer model specified by the model_name parameter.
• The model is deployed directly onto the selected device (GPU/CPU), optimizing performance during embedding computation.

Model: sentence-transformers/all-mpnet-base-v2

Summary

sentence-transformers/all-mpnet-base-v2 is a state-of-the-art, pretrained sentence embedding model that transforms full sentences or paragraphs into fixed-length 768-dimensional vectors. Designed for semantic search and similarity tasks, it supports handling long-form content, making it a strong fit for evaluating webpage sections in SEO contexts.

Technical Details

• Architecture: Based on Microsoft’s MPNet model, which blends masked and permuted language modeling to capture bidirectional context like BERT while avoiding token position issues seen in XLNet.
• Embedding Dimension: Each text input is encoded into a 768-dimensional vector.
• Input Limit: Trained with a maximum of 384 token sequence length.
• Training Data: Fine-tuned on over 1 billion sentence pairs using contrastive learning to optimize semantic similarity performance.
• Performance Benchmarks: Among the top-quality models in the Sentence Transformers family, particularly strong for semantic search, clustering, and ranking tasks.

How It Works

1. Tokenization and Transformer Encoding: Text is tokenized and passed through the MPNet-based Transformer encoder.
2. Pooling Layer: Outputs are pooled (mean pooling) into a single numeric vector, representing the entire input segment.
3. Embedding Normalization: Embeddings are normalized and used for similarity comparisons via cosine similarity.

The architecture is ideal for capturing nuanced meaning from headings, paragraphs, and long content blocks—precisely what an SEO ranking interpretability system needs.

Benefits for SEO Use Cases

• Contextual Relevance Detection: Unlike simple keyword matching, the model scores content blocks based on deeper semantic alignment, supporting more trustworthy ranking explanations.
• Long-form Handling: Able to embed larger text segments like paragraphs or sections, making it suitable for ranking and interpreting real-world webpages.
• Proven Benchmark Performance: High performance on global datasets (e.g., MS MARCO, Wikipedia similarity, clustering benchmarks) lends reliability to its SEO applications.

Why It’s Used in This Project

• Supports Interpretability Goals: Embeddings enable using cosine similarity in conjunction with keyword and heading metrics for transparent block scoring.
• Seamless Integration: Easy to load through SentenceTransformer(), integrates well with a Google Colab-driven modular pipeline, no heavy fine-tuning needed.
• Balanced Efficiency: Though powerful, the base-size (768 dims) model remains feasible on standard GPU/CPU environments, helpful in client-oriented tools.

Function get_query_embedding()

Function Summary

The get_query_embedding() function generates a numerical embedding representation for a given query string using a preloaded SentenceTransformer model. This embedding captures the semantic meaning of the query, enabling advanced tasks such as semantic search, similarity comparison, and intent matching. The function supports both CPU and GPU execution, and it can handle input provided as either a string or a list.

Key Code Explanations

1. Device Selection

• If no device is explicitly provided (‘cpu’ or ‘cuda’), the function automatically detects the device the model is currently loaded on.
• This avoids mismatch errors between model and tensor devices.

2. Encoding Method

• Primary method: Attempts to use encode_query() — available in newer SentenceTransformer versions for query-specific optimizations.
• Fallback method: If encode_query() is not available (older versions), it defaults to the generic encode() method.
• The [0] index extracts the tensor for the single query from the returned batch.

Function Summary

The get_block_embedding() function generates an embedding representation for a given text block using a preloaded SentenceTransformer model. This embedding captures the semantic meaning of the content block, enabling downstream tasks such as semantic similarity measurement, intent alignment, and relevance scoring. The function supports execution on both CPU and GPU and works with inputs provided as either a string or a list.

Key Code Explanations

1. Input Normalization

• Ensures that the block content is processed in list form, which is the standard input format for SentenceTransformer encoding methods.
• This standardization prevents errors and allows batch processing if needed.

2. Device Selection

• Automatically detects the device (‘cpu’ or ‘cuda’) the model is loaded on if no device is explicitly provided.
• Prevents device mismatch errors between model parameters and tensors.

3. Encoding with SentenceTransformer

encoded = model.encode(block_text, convert_to_tensor=True, device=device)[0]

• Uses the generic encode() method from SentenceTransformer to generate embeddings for the block content.
• Since content blocks don’t require query-specific optimizations, encode() is used directly rather than encode_query().
• [0] extracts the embedding tensor for the single block from the returned batch.

Function Summary

The compute_similarity_scores() function calculates the cosine similarity between a single query embedding and multiple content block embeddings. This metric quantifies how semantically close each block is to the query, with scores ranging from -1 (opposite meaning) to 1 (identical meaning). The function returns a list of similarity scores for all blocks, enabling downstream tasks like ranking, filtering, and intent alignment.

Key Code Explanations

1. Cosine Similarity Computation

similarities = util.cos_sim(query_embedding, block_embeddings)

·         Uses the cos_sim() method from sentence_transformers.util to compute similarity scores.

·         This method internally:

• Normalizes both input embeddings.
• Computes the dot product to obtain cosine similarity.

·         Operates between:

• query_embedding: A single query vector (1 x D).
• block_embeddings: Multiple block vectors (N x D).

·         Produces a result matrix of shape (1 x N).

2. Flattening the Result

return similarities.squeeze(0).tolist()

• squeeze(0) removes the extra first dimension, converting shape (1 x N) to (N,).
• .tolist() converts the tensor into a Python list for easier handling in downstream logic, such as ranking or JSON serialization.

Function: generate_query_metadata()

Function Summary

The generate_query_metadata() function processes a provided list of search queries and generates a structured metadata dictionary for each query. This metadata includes a unique query ID, the query text, and an initially empty placeholder for synonyms or related terms. The output is intended to be used as the foundational query reference throughout the analysis pipeline, ensuring consistent query tracking and enabling future enrichment steps such as synonym expansion or embedding generation.

Key Code Explanations

1. Enumerate Through Queries

for idx, q in enumerate(queries):

• Loops through the list of queries while also retrieving an index (idx) for each query.
• idx ensures every query is given a unique identifier in the metadata.
• Enumeration makes the process dynamic — the function can handle any length of query list.

2. Append Structured Metadata for Each Query

• Creates a dictionary for each query with:
  • “query_id” — the unique numeric identifier for tracking.
  • “query_text” — the original query text as given by the client.
  • “synonyms” — an empty list, ready to be populated in future processing steps.
• The use of a dictionary ensures flexibility for storing future metadata fields without changing the core pipeline.

Function calculate_weighted_scores()

Function Summary

The calculate_weighted_scores() function computes a weighted final relevance score for each content block within a page dataset. It combines multiple contributing factors — semantic similarity, keyword match ratio, and heading match importance — based on predefined or provided weights. The function also stores a detailed factor-by-factor breakdown for transparency and later explanation.

Key Code Explanations

1. Default Weights Handling

• If no custom weights are provided, the function applies default values:
  • Similarity: 0.7 (most influential factor)
  • Keyword Match: 0.2
  • Heading Match: 0.1
• This ensures that even without explicit user-defined weights, the scoring process remains consistent.

2. Extracting Page-Level Metadata

• Retrieves page-level metrics from query_metadata:
  • keyword_match_ratio: Proportion of query keywords found across the page.
  • heading_match: Indicates if relevant terms appear in headings or prominent sections.
• Defaults to 0 if data is missing, preventing computation errors.

3. Per-Block Weighted Scoring

• For each content block:
  • Multiplies the block’s semantic similarity score by its assigned weight.
  • Multiplies the keyword match ratio by its weight.
  • Multiplies the heading match score by its weight.
• Adds the three weighted components to produce the final relevance score.

4. Storing Factor Breakdown and Final Score

• Saves:
  • score_breakdown: Rounded values for each factor’s weighted contribution.
  • final_score: The total computed relevance score for the block.
• Rounding to 5 decimal places maintains precision without clutter.

Function generate_factual_explanation()

Function Summary

The generate_factual_explanation() function creates a clear, human-readable explanation for each content block within a page, based on its semantic similarity score, keyword matches, and whether it belongs to an important structural section (such as a heading). This explanation provides transparency for why a block was considered relevant in search or ranking processes. The resulting explanations are stored in each block’s metadata.

Key Code Explanations

1. Iterating Through Content Blocks

• Loops through each content block in the provided page_data dictionary.
• Each block will receive its own individual factual explanation.

2. Retrieving Scoring and Metadata Values

• sim_score — Semantic similarity score for the block.
• keyword_match — Ratio of matched keywords from the query.
• heading_match — Indicates if the block belongs to a heading or important section.

3. Extracting Matched Keywords

• Retrieves keywords from the query that are present in the block.
• If keywords are found, they are joined into a comma-separated string; otherwise “none” is used.

4. Building the Explanation Parts

• Creates individual sentence fragments based on available data:
  • Similarity score explanation.
  • Keyword match explanation.
  • Heading relevance explanation.

5. Combining and Storing the Explanation

• Joins the generated explanation parts into one complete string.
• Stores the explanation under the “factual_explanation” field of the block.

Function score_blocks_pipeline()

Function Summary

The score_blocks_pipeline() function orchestrates the full block scoring process for a single query. It enriches the page data with query metadata, computes semantic similarity scores for each block, applies weighted scoring based on predefined factors, generates factual explanations, and calculates an overall page-level score. The result is a structured page_data dictionary with blocks sorted by relevance and transparent reasoning behind their ranking.

Key Code Explanations

1. Generating Query Metadata

• Enriches page_data with metadata about the query, such as matched terms and keyword ratios.
• This step is essential for downstream keyword matching and weighted scoring.

2. Extracting Block Text and Generating Embeddings

• block_texts — Collects the raw text from each content block.
• query_embedding — Encodes the search query into a semantic vector.
• block_embeddings — Encodes each block’s text into semantic vectors for comparison.

3. Computing Similarity Scores

scores = compute_similarity_scores(query_embedding, block_embeddings)

• Uses a similarity function (e.g., cosine similarity) to determine how close each block is to the query in semantic space.
• Produces a list of raw similarity scores.

4. Applying Weighted Scoring

• Wraps updated data into a new result dictionary.
• Calls calculate_weighted_scores() to compute a “final_score” for each block, incorporating similarity, keyword matching, and heading match weights.
• Re-sorts blocks by “final_score” for final ranking.

5. Generating Factual Explanations

result = generate_factual_explanation(result)

• Adds a “factual_explanation” to each block, describing why it ranked where it did.

6. Calculating Page-Level Score

• Selects the top top_k blocks (default 5) based on final score.
• Computes the page_score as the average of these top blocks’ scores.
• Ensures the overall page score reflects the performance of its most relevant content.

Function clean_query_list()

Function Summary

The clean_query_list() function standardizes and cleans a list of user-provided search queries. It ensures that each query is stripped of unnecessary whitespace, converted to lowercase for uniformity, and filters out any empty or invalid entries. This preprocessing step is essential to maintain data consistency before performing downstream NLP tasks like intent classification, embedding generation, or semantic matching.

Key Code Explanations

1. Input Validation and Cleaning

• Ensures only valid string entries are processed, excluding None, numeric values, or non-string objects.
• q.strip() removes leading and trailing whitespace and also acts as a filter — entries with no characters after stripping (e.g., empty strings or spaces) are discarded.
• .strip() removes any extra spaces from the start and end of the query.
• .lower() converts the query to lowercase, enabling case-insensitive matching in later processing steps.
• Processes all queries in a single, compact expression for efficiency.
• Only valid, non-empty strings are transformed and included in the returned list.
• The final result is a clean, uniform list of queries ready for analysis or indexing.

Function score_page_by_queries()

Function Summary

The score_page_by_queries() function evaluates a single webpage’s content against multiple search queries by leveraging the score_blocks_pipeline() function. For each query, it generates a version of the page_data enriched with block-level similarity scores. This allows SEO professionals to see how well different parts of a page align with various user intents, enabling precise optimization strategies for multi-keyword targeting.

Key Code Explanations

1. Iterating Over Multiple Queries

• Loops through each query in the provided queries list.
• Calls the score_blocks_pipeline() function for every query, ensuring that scoring logic remains centralized and consistent.
• Returns a list where each element corresponds to the scoring result for one query.
• The function takes a single page_data object from extract_clean_blocks(), ensuring that the page is already cleaned and structured into blocks before scoring.
• This avoids redundant parsing and ensures all queries are scored against the exact same page structure.

Function load_generation_model()

Function Summary

The load_generation_model() function initializes and returns a Hugging Face transformers text-generation pipeline using a specified model. This model can be used for tasks such as content generation, summarization, or instruction-following, depending on the model’s capabilities. The function automatically detects the available device (GPU or CPU) to optimize performance and ensures the model is loaded in a ready-to-use state.

Key Code Explanations

1. Default Model Selection

• The function allows specifying a custom model name, but defaults to “tiiuae/Falcon-E-3B-Instruct”.
• This default is a quantized instruction-tuned Falcon model, which is suitable for efficient inference in constrained environments.

2. Pipeline Initialization

• Loads the Hugging Face text-generation pipeline with the given model name and device.
• Ensures the model is ready for text generation tasks in a single step, without requiring additional configuration.

Model: tiiuae/Falcon-E-3B-Instruct

Model Overview

The tiiuae/Falcon-E-3B-Instruct is a lightweight, instruction-tuned language model developed by the Technology Innovation Institute (TII). Designed for efficient deployment on edge devices, it offers a balance between performance and resource utilization, making it ideal for applications requiring real-time text generation and explanation polishing. This model is particularly suited for tasks such as content summarization, question answering, and generating concise explanations from factual data.

Technical Specifications

• Architecture: Causal decoder-only transformer model
• Parameter Count: Approximately 3 billion
• Quantization: Utilizes a 1.58-bit quantization technique for reduced memory usage
• Context Length: Supports up to 32,000 tokens
• Memory Footprint: Approximately 955 MB, optimized for edge devices
• Precision: Supports bfloat16 and 8-bit precision modes
• Languages Supported: Primarily English

How It Works

The Falcon-E-3B-Instruct model is fine-tuned using instruction-based datasets, enabling it to follow user prompts effectively. Its transformer architecture processes input sequences and generates coherent, contextually relevant outputs. The 1.58-bit quantization reduces the model’s size without significantly compromising performance, making it suitable for deployment on devices with limited computational resources.

Key Benefits

• Edge Optimization: Its compact size and efficient processing capabilities make it ideal for deployment on edge devices, reducing latency and bandwidth usage.
• Cost Efficiency: Lower memory and computational requirements translate to reduced operational costs.
• Real-Time Processing: Capable of generating responses quickly, facilitating real-time applications.
• Versatility: Suitable for a wide range of NLP tasks, including summarization, question answering, and content generation.

Relevance to SEO Projects

In SEO-focused applications, the Falcon-E-3B-Instruct model can be utilized to generate and polish explanations for content blocks, enhancing the clarity and relevance of information presented to users. Its ability to process and generate text efficiently makes it a valuable tool for improving user engagement and satisfaction.

Integration in This Project

In this project, the Falcon-E-3B-Instruct model is employed to refine factual explanations extracted from content blocks. By generating concise and coherent explanations, the model aids in presenting information in a user-friendly manner, thereby enhancing the overall quality and effectiveness of the content.

Function generate_explanations_for_ranked_pages()

Function Summary

The generate_explanations_for_ranked_pages() function refines factual block-level explanations into concise, natural-language sentences using a text generation model. It processes only the top-ranked content blocks for each page, based on a specified limit, and ensures the output follows strict formatting rules. This step enhances the interpretability of ranked search results by producing human-readable relevance justifications.

Key Code Explanations

1. Iterating Through Pages and Selecting Top Blocks

• Iterates over each page’s data in the results_data list.
• Extracts the associated query and selects only the top_k highest-ranked content blocks for processing.

2. Prompt Construction for the Generation Model

• Builds a structured prompt with clear instructions for the generation model.
• Includes the block’s text, the search query, and the factual explanation as input context.
• Adds strict rules to ensure the generated text is concise, relevant, and properly formatted.

3. Generating and Storing Polished Explanations

• Calls the text generation model with the constructed prompt.
• Limits output length to 50 tokens to enforce conciseness.
• If the model produces a result, it replaces the block’s explanation with the polished version; otherwise, it falls back to the original factual explanation.

Result Analysis and Explanation

Page-Level Score

The page demonstrates a moderate relevance to the query, reflected by a page score of approximately 0.57. This indicates that the content provides useful guidance but could be strengthened to fully satisfy the query intent.

Top Relevant Content Blocks

The top scoring blocks focus on actionable instructions and best practices for handling different document URLs using HTTP headers:

• Implementing Canonical Tags: Provides clear steps to apply canonical tags for PDFs, images, and videos, which is essential for proper indexing and avoiding duplicate content issues.
• Ensuring URL Quality: Advises maintaining the most relevant and high-quality version of URLs, reinforcing best practices for content management.
• Step-by-Step Guidance: Includes specific instructions to identify URLs needing canonicalization and update them appropriately when files are moved or replaced.
• Avoiding Excessive Redirects: Highlights the importance of properly structured URLs to prevent broken links and ensure content accessibility.

Interpretation

• The highest scoring block (0.60) provides direct, actionable guidance that aligns closely with the query.
• Other top blocks (0.54–0.58) reinforce the main guidance and provide supplementary information to ensure comprehensive coverage.
• Collectively, these blocks form a coherent set of instructions for managing document URLs effectively, with practical steps that can be directly implemented.

Insights

• The content addresses key query aspects, focusing on canonicalization, URL quality, and structural integrity.
• Emphasizing top scoring blocks and ensuring completeness of guidance can further improve the page’s relevance.
• Clear structuring and stepwise instructions enhance usability and make it easier to follow the recommendations.

Result Analysis and Explanation

Page-Level Scores and Interpretation

Overview of Page Relevance Page-level relevance scores provide a holistic view of how well the content of an entire page aligns with a given search query. These scores are computed by aggregating the relevance of the top content blocks, highlighting whether the page is likely to satisfy user intent in its current form.

Score Interpretation Guidelines

• High Score (≥ 0.75) — The page contains highly relevant content, offering actionable insights and clear guidance.
• Moderate Score (0.60–0.74) — Content is partially aligned with the query but may lack depth, specificity, or clarity.
• Low Score (0.45–0.59) — The page provides tangential or surface-level information that only partially addresses the query.
• Minimal Score (< 0.45) — Page is mostly off-topic; significant content restructuring or supplementation may be needed.

Practical Implications High-scoring pages serve as key assets for SEO campaigns and promotion. Pages with moderate or low scores identify opportunities for content refinement, reorganization, or targeted expansion to better meet search intent.

Content Block-Level Insights

Understanding Block Relevance Individual content blocks are scored for their contribution to answering the query. Scores are based on semantic similarity, keyword coverage, and alignment with structural cues such as headings. These scores provide a granular view of content performance within the page.

Interpreting Block Scores

• High-Scoring Blocks — Directly address the query with precise, actionable, and detailed information.
• Moderate-Scoring Blocks — Provide supporting context or partial answers but may require refinement for clarity or completeness.
• Low-Scoring Blocks — Contain peripheral or background information and are less central to query satisfaction.

Actionable Guidance Focus on high-scoring blocks for highlighting key messages, internal linking, and structured schema. Moderate blocks are candidates for targeted improvement, while low-scoring blocks can be restructured, merged, or removed to streamline the page focus.

Explanations and Interpretability

Polished Explanations Text generation models produce concise explanations for each block, translating raw relevance signals into human-readable summaries. These explanations clarify why a block contributes to page relevance and support editorial validation.

Benefits for Decision-Making

• Enhances transparency of automated relevance scoring.
• Enables content teams to quickly evaluate and refine content strategy.
• Provides stakeholders with actionable insights into page performance relative to search queries.

Visualization and Insights

Aggregated Page Scores Visualizations present page-level relevance across multiple queries, making it easy to identify consistently high-performing pages versus those requiring optimization.

Score Component Breakdown Visual analysis of semantic similarity, keyword alignment, and heading match illuminates the factors driving block and page scores. This reveals whether gaps in performance are due to content clarity, keyword coverage, or structural organization.

Interpretation Pages with high scores across components indicate coherent, well-structured content aligned with search intent. Lower component scores point to targeted improvements, such as refining headings, enhancing keyword integration, or strengthening semantic context.

Recommended Actions

Immediate (High Impact, Low Effort)

• Highlight and promote high-scoring pages and blocks through internal linking, structured data implementation, and feature snippets.
• Use polished explanations to guide rapid editorial reviews and minor content adjustments.

Near-Term Improvements (Moderate Effort, High Impact)

• Refine moderate-scoring blocks by adding clarity, examples, and additional detail to elevate relevance.
• Evaluate low-scoring blocks to determine whether they can be merged, restructured, or removed to improve overall page focus.

Strategic Considerations (Ongoing)

• Implement a periodic monitoring workflow for page and block-level relevance scores to maintain alignment with evolving search intent.
• Leverage aggregated visual insights to guide long-term content planning and topic clustering, ensuring pages support a coherent, user-focused SEO strategy.

Operational Notes

• Relevance scores provide a scalable, data-driven proxy for content quality and alignment, but manual editorial review remains essential for nuanced decision-making.
• Visual dashboards transform complex scoring signals into actionable insights, supporting efficient prioritization of content improvements.
• The focus is on aligning page structure and content with likely user intent, rather than purely maximizing numerical scores.

What are the main features of this analysis project?

The project evaluates the alignment between a page’s content and the target queries using a hybrid scoring approach. Each page is analyzed at a granular level with content blocks receiving relevance scores that reflect semantic similarity, keyword presence, and structural importance. Additionally, natural language explanations for each block provide interpretability, ensuring transparency in why a particular section is deemed highly relevant. The pipeline also aggregates block-level data into page-level scores, enabling a holistic view of page performance. Visual insights from aggregated scores and component breakdowns allow clients to quickly identify strengths and weaknesses across URLs and queries.

How does the project help in assessing page-level relevance?

Page-level relevance is determined by aggregating block-level scores, considering both content significance and alignment with query intent. This highlights pages that are most likely to satisfy user queries effectively. The analysis identifies top-performing content blocks and quantifies their contributions to overall page relevance. Clients can prioritize high-scoring pages for promotion, internal linking, or featured snippets while recognizing low-performing pages or blocks that require optimization. The visualizations of page scores across queries provide a comparative understanding of which pages perform best for specific intents.

How can clients use block-level insights effectively?

Block-level relevance scores and explanations give clients actionable granularity. Highly scoring blocks indicate core content that effectively satisfies query intent, which can be further leveraged through internal linking, schema markup, or content amplification. Moderate-scoring blocks highlight opportunities for optimization, such as clarifying headings, adding examples, or enhancing keyword coverage. Low-scoring blocks may dilute intent and should either be revised, split, or removed. The explanation text provides context and guidance for each block, reducing guesswork and accelerating actionable decision-making.

What value do the visualizations add to the analysis?

Visualizations translate quantitative signals into easily interpretable insights. Aggregated page scores show which URLs perform best across different queries, helping prioritize optimization efforts. Score breakdown plots indicate which components—semantic similarity, keyword match, or heading alignment—contribute most to relevance. This allows clients to understand why certain pages underperform and where targeted improvements are needed. By combining visual summaries with detailed block-level explanations, the project provides both strategic and tactical guidance for SEO planning.

How can the project support content strategy decisions?

The analysis identifies high-value sections that drive page relevance, enabling clients to reinforce or expand them in their content strategy. Moderately scoring sections can be enhanced to boost overall page performance, while low-value sections may need to be repurposed or removed to avoid intent dilution. By monitoring page and block scores across queries, clients can plan content clusters, optimize for multi-intent coverage, and ensure consistency in search intent alignment. The result-driven insights reduce risk of misaligned content and improve ROI for content updates and creation.

How should clients act on pages with mixed performance scores?

Pages exhibiting a mix of high, moderate, and low scores should be treated with a nuanced strategy. High-scoring blocks should be highlighted and possibly linked internally or featured in snippets. Moderate blocks should undergo targeted improvements such as rewriting, clarifying headings, or adding examples. Low-scoring content that is off-topic or redundant should be either split into dedicated pages or consolidated with more relevant material. Visualization dashboards assist in quickly identifying pages with uneven performance and determining where intervention yields the highest impact.

How can the explanations generated by the model improve operational efficiency?

Explanations attached to each content block provide reasoning behind the scoring, allowing SEO teams to understand which elements drive relevance without manually evaluating each section. This reduces time spent on content auditing, accelerates decision-making, and ensures consistency in optimization strategies. Explanations highlight key signals such as semantic alignment and keyword coverage, enabling teams to take precise, actionable steps rather than relying solely on intuition or generic metrics.

How can this analysis be integrated into ongoing SEO workflows?

The project’s outputs can be embedded into regular SEO audits, content planning, and page optimization cycles. Page-level scores and visual summaries provide a quick overview of performance, while block-level scores and explanations enable fine-grained interventions. By using the insights in tandem, clients can prioritize high-impact updates, track performance improvements over time, and validate the effectiveness of new content or structural changes. The system promotes a continuous improvement loop, enhancing discoverability, engagement, and search engine rankings efficiently.

Final Thoughts

The project establishes a transparent framework for understanding why pages achieve high rankings by generating interpretable explanations for content performance. Combining block-level relevance scoring, semantic similarity, keyword and heading evaluation, and natural language explanations, the system provides clear insights into the factors driving ranking outcomes. Page-level aggregation and visualization of scores allow a structured overview of content alignment with search queries, highlighting areas of strength and opportunities for improvement.

This interpretability enables identification of high-impact content blocks, refinement of moderately performing sections, and addressing of tangential or low-value content efficiently. By providing explicit reasoning behind ranking signals, the framework enhances trust in automated assessments and facilitates informed optimization strategies that strengthen content relevance and overall ranking effectiveness.

Ultimately, the approach supports a systematic, data-driven method for monitoring, analyzing, and improving page performance. The explanations and visualizations transform quantitative ranking signals into actionable insights, ensuring content structure, coverage, and intent alignment are transparent and measurable.

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Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker.

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