Improving Content-Based Similarity and Scoring Models with Transformer Embeddings

Introduction

In the world of natural language processing (NLP) and content analysis, measuring similarity between articles and evaluating the impact of journalists or podcast creators has become increasingly important. With the rise of embedding-based models, we now have powerful tools to quantify semantic relationships and rank content relevance. However, effectively leveraging these models requires a deep understanding of distance metrics, dimensionality challenges, and scoring methodologies.

This article explores key considerations in choosing and applying distance metrics — such as cosine similarity, Euclidean, and Manhattan distances — for evaluating article embeddings. It also addresses common pitfalls in high-dimensional spaces and offers practical strategies to improve similarity ranking, including fine-tuning models, reducing dimensionality, and combining multiple metrics.

Furthermore, we present a detailed code review of a journalist and podcast scoring system, identifying potential issues related to normalization, weighting, manual adjustments, and handling edge cases. Practical recommendations are provided to enhance the robustness, fairness, and interpretability of the scoring pipeline.

Whether you’re building a recommendation system, content search engine, or media analytics platform, this guide aims to equip you with the insights and best practices needed to refine your similarity and scoring models effectively.

Choosing a Distance Metric

Common distance metrics for evaluating similarities between embeddings include:

• Cosine Similarity: Measures the cosine of the angle between two non-zero vectors. It’s effective for high-dimensional spaces and is often used for text embeddings.

• Euclidean Distance: Measures the straight-line distance between two points in the embedding space. It’s less sensitive to the magnitude of the vectors.

• Manhattan Distance: Measures the distance between points along axes at right angles. It can be useful in some contexts but is less common for text embeddings.

High-Dimensional Space: In high-dimensional embeddings, the phenomenon known as the “curse of dimensionality” can occur. As dimensionality increases, points tend to become more equidistant from each other, which might make it harder to distinguish truly similar articles.

Contextual Meanings: If the articles are about different topics but share similar word usage, the cosine similarity might show them as closely related. This is especially true if the embeddings capture syntactic rather than semantic similarity.

Vector Magnitude: Cosine similarity focuses on the direction of the vectors rather than their magnitude. If two articles have similar themes but different lengths (and hence different vector magnitudes), they might be assigned higher similarity scores.

Tips to Improve Similarity Ranking

1. Fine-Tune Embeddings: If using a transformer-based model, consider fine-tuning it on a domain-specific dataset to capture more relevant semantic relationships.

2. Combine Metrics: Use a combination of cosine similarity and other metrics (like Euclidean distance) to get a more nuanced view of similarity.

3. Preprocessing: Ensure that your text data is well-preprocessed (removing stop words, stemming, etc.) to improve the quality of embeddings.

4. Dimensionality Reduction: Before computing similarities, apply techniques like PCA or t-SNE to reduce dimensions while preserving variance. This might help in grouping similar articles more effectively.

5. Clustering: Use clustering algorithms (like K-means or hierarchical clustering) on your embeddings to group similar articles and then examine the clusters.

6. Thresholding: Establish a threshold for cosine similarity scores to filter out articles that are not sufficiently similar.

7. Evaluation: Regularly evaluate your results against a labeled dataset to fine-tune your approach and metrics.

Using the all-MiniLM-V2 model, which generates embeddings with 384 dimensions, can indeed be effective for many tasks, including measuring article similarities. However, there are a few considerations related to the dimensionality that might impact your results:

Potential Issues with 384-Dimensional Embeddings.

1. Curse of Dimensionality: In high-dimensional spaces, data points become more spread out, and the concept of distance can become less meaningful. This can sometimes lead to results where items that should be similar are not ranked highly in similarity.

2. Dense Representation: While 384 dimensions is relatively low compared to other transformer models, it’s still enough to capture nuanced relationships. However, if the embeddings do not adequately differentiate between articles, you may find that articles with less relevance appear similar.

3. Vector Distribution: If your dataset is imbalanced or lacks diversity, the embeddings might not capture the full range of relationships. This can lead to misleading similarities.

   
   

Recommendations

1. Experiment with Other Models: If you find issues with the all-MiniLM-V2 embeddings, consider experimenting with other models that provide embeddings tailored for your specific domain or task.

2. Evaluate with Clustering: Use clustering techniques to see if articles naturally group together. This can help identify whether the embeddings are capturing meaningful similarities.

3. Analyze Results: Look into the top similar articles for a few selected queries. Are they genuinely similar in content? If not, this might indicate a need for a different approach.

4. Use Ensemble Approaches: Combine similarities from multiple models or metrics (like cosine similarity and Euclidean distance) to refine your ranking.

5. Preprocessing: Ensure thorough preprocessing of your text data to enhance the quality of the embeddings.

6. Tuning the Threshold: Experiment with different thresholds for cosine similarity to find a balance that works best for your specific dataset.

   
   

Potential Issues in code review

The code you shared defines a scoring system for journalists and podcasts based on the relevance and quantity of their articles or episodes. Here’s an analysis of potential issues with the quantification of scores:

1. Normalization: The code normalizes the quantity factor by dividing the article count for each journalist by the maximum article count across all journalists.

   
   

Problem: This approach might not be ideal if there’s a significant skew in the distribution of article counts. A journalist with a very high article count could disproportionately influence the normalization, potentially leading to lower scores for others.

Solution: Consider using a more robust normalization method, such as min-max scaling or standardization (z-score normalization) to mitigate the impact of outliers.

2. Weighting: The code uses fixed weights (quantity_factor and quality_factor) to combine the quantity and quality factors.

Problem: The chosen weights might not be optimal and could lead to an imbalance between quantity and quality in the final score.

Solution: Experiment with different weight combinations to find the balance that best reflects the desired scoring criteria. Consider using techniques like grid search or Bayesian optimization to optimize the weights based on evaluation metrics.

3. Manual Score Adjustment: The manualscore_adjustment function adds 0.2 to the original score and caps the result at 0.97.

Problem: This adjustment introduces a non-linearity in the scoring and lacks transparency. The rationale for this specific adjustment isn’t clear, and it might lead to unexpected results or biases.

Solution: If score adjustments are necessary, consider using a more transparent and justifiable method, such as applying a sigmoid function or a piece wise linear function. Clearly document the reasons for any adjustments and their potential impact on the scores.

4. Missing Date Handling: The code has a potential issue in calculate_journalist_score where avg_score might result in a division by zero if a journalist has no articles.

Problem: This would lead to an error or undefined behavior.

Solution: Add a check to handle cases with zero articles, for example by setting avg_score to 0 if articles are empty.

5. Hard coded Threshold in find_similar_journalists: The code uses a hard coded threshold (0.5) for filtering articles and a fixed number (5) for keeping top articles when all are below the threshold.

Problem: These hard coded values might not generalize well to different datasets or scenarios.

Solution: Consider making these values configurable or using a more dynamic approach, such as setting the threshold based on the distribution of scores or using a percentile-based approach for selecting top articles.

6. Data point Missing Date Error: The code logs an error if a data point is missing a date.

   
   

Problem: While logging the error is useful, it doesn’t handle the missing date, potentially leading to inaccurate or missing scores.

Solution: Implement a strategy for handling missing dates, such as using a default date, imputing the date based on other data points, or excluding data points with missing dates (with appropriate logging).

Recommendations:

• Thorough Testing: Test the scoring system with various datasets and scenarios to identify any biases or unexpected behavior.

• Documentation: Clearly document the scoring methodology, including normalization, weighting, and any adjustments made.

• Regular Evaluation: Periodically evaluate the effectiveness of the scoring system and make adjustments as needed. Consider using metrics such as precision, recall, or NDCG (Normalized Discounted Cumulative Gain) to assess the performance.

• Consider Alternatives: Explore alternative scoring methods or ranking algorithms, such as collaborative filtering or content-based filtering, to see if they better suit the specific needs of the application.

• By addressing these potential issues and following the recommendations, you can improve the robustness, fairness, and transparency of the scoring system.