This blog explores data science and networking, combining theoretical concepts with practical implementations. Topics include routing protocols, network operations, and data-driven problem solving, presented with clarity and reproducibility in mind.
Imagine handling thousands of product listings and advertisements daily. The ability to automatically match ads to the correct products is not just convenient—it’s essential for modern digital platforms.
๐ก Goal: Match each advertisement to the most relevant product—or return no match if similarity is low.
๐ Understanding the Problem
Inputs
Product Data (title, description, images)
Advertisement Data (title, description, images)
Outputs
(ad_id, product_id)
(ad_id, None)
๐ Expand Explanation
If no product crosses the similarity threshold, we explicitly return None to avoid incorrect mapping.
⚠️ Challenges
Data inconsistency – informal vs formal descriptions
Multimodal data – text + images
Scalability – large datasets
Threshold tuning – subjective similarity cutoffs
๐ก Real-world systems fail more due to bad thresholds than bad models.
๐งฉ Solution Overview
Preprocess data
Compute similarity
Match and filter
๐ Data Preprocessing
Text Processing
Tokenization
Lowercasing
Stopword removal
Lemmatization
Embedding Techniques
TF-IDF
Word2Vec
BERT / Sentence-BERT
Image Processing
Use CNN models (ResNet, EfficientNet)
Extract feature vectors
๐ Similarity Measurement
Cosine Similarity Formula
cos(ฮธ) = (A · B) / (||A|| ||B||)
This measures how similar two vectors are based on angle rather than magnitude.
Euclidean Distance
d = √(ฮฃ (xi - yi)^2)
Multimodal Combination
Final Score = w1 * text_similarity + w2 * image_similarity
๐ Why Combine Modalities?
Text alone may miss visual similarity. Images alone may miss context. Together, they give better accuracy.
⚙️ Machine Learning Pipeline
Feature extraction (text + image)
Embedding storage
Similarity computation
Ranking + threshold filtering
๐ก Precompute embeddings to improve speed drastically.
๐ป Code Example
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
model = SentenceTransformer('all-MiniLM-L6-v2')
ad_embedding = model.encode(ad_text)
product_embedding = model.encode(product_text)
score = cosine_similarity([ad_embedding], [product_embedding])
print(score)
๐ฅ CLI Output Example
Processing Ads...
Embedding Generated ✔
Calculating Similarity...
Ad 101 → Product 55 (Score: 0.87)
Ad 102 → None (Score: 0.32)
๐ Expand CLI Explanation
Scores above threshold (e.g., 0.75) are accepted. Others are rejected to avoid false matches.
๐ง Common Issues & Solutions
1. Data Imbalance
Use precision/recall instead of accuracy.
2. Noisy Data
Apply spell correction and filtering.
3. Performance
Use FAISS for fast nearest neighbor search.
4. Threshold Problems
Use validation data for tuning.
๐ฏ Key Takeaways
Multimodal learning improves accuracy
Embeddings are the foundation
Threshold tuning is critical
Scalability requires smart indexing
๐ Final Thoughts
Matching advertisements to products is not just a machine learning task—it’s a system design challenge. The best solutions combine strong modeling, efficient computation, and continuous evaluation.
If implemented correctly, this approach can significantly improve automation, reduce manual effort, and enhance user experience in any data-driven platform.
Financial News Summarization using NLP and BLEU Score Evaluation
Financial News Summarization using NLP and BLEU Score Evaluation
Natural Language Processing (NLP) has transformed how we analyze large amounts of text data. One important application of NLP is automatic text summarization, where lengthy articles are condensed into shorter summaries while preserving key information.
In finance, news arrives continuously from multiple sources. Investors, analysts, and traders often struggle to read every article related to stock markets. Automatic summarization helps reduce information overload by generating concise summaries from large collections of financial news.
In this tutorial, we will explore a complete NLP pipeline that:
In this tutorial, we explored how NLP can automatically summarize financial news articles and evaluate summary quality using BLEU score.
The workflow included:
Fetching financial news
Preprocessing text
Building similarity matrices
Applying KMeans clustering
Generating summaries
Evaluating summaries mathematically
This demonstrates how machine learning and NLP techniques can transform massive amounts of textual information into concise actionable insights.
As financial markets continue generating enormous volumes of data, intelligent summarization systems will become increasingly important for investors, analysts, and automated decision-making systems.
