A news aggregator can use Sentence Transformers to group related articles or recommend similar content by converting text into semantic vector representations. Sentence Transformers generate dense embeddings that capture the meaning of sentences or paragraphs, allowing the system to measure similarity between articles mathematically. For example, articles about "climate change policy" would have vectors closer in the embedding space than those about "sports events," enabling automated grouping or recommendations based on semantic relevance.
Grouping Articles:
The aggregator first processes article text (titles, summaries, or key paragraphs) through a Sentence Transformer model like all-MiniLM-L6-v2 to generate embeddings. These embeddings are then clustered using algorithms such as K-means, DBSCAN, or hierarchical clustering. For instance, articles covering the same event (e.g., a political summit) but from different publishers would cluster together because their semantic vectors are similar. To handle dynamic news, the system might update clusters incrementally or use online clustering techniques. This approach reduces redundancy and helps users quickly find coverage of the same topic from multiple sources.
Recommending Similar Content:
For recommendations, the aggregator compares a user’s current article embedding with embeddings of other articles using cosine similarity or approximate nearest neighbor search (ANN) tools like FAISS or Annoy. For example, if a user reads an article about a SpaceX rocket launch, the system would recommend articles with embeddings closest to that article’s vector, such as updates on the mission or related space industry news. This method works across languages if a multilingual model (e.g., paraphrase-multilingual-MiniLM-L12-v2) is used, enabling recommendations for articles in the user’s preferred language even if the source content is multilingual.
Handling Scale and Noise: To ensure efficiency, the aggregator might precompute embeddings during article ingestion and cache results. For noisy data (e.g., clickbait headlines), techniques like filtering low-confidence embeddings or combining metadata (e.g., publisher categories) with semantic similarity can improve accuracy. For example, two articles titled "Market Crash Imminent" might be grouped only if their body text embeddings align, avoiding false matches. This hybrid approach balances semantic understanding with practical constraints like compute resources and real-time performance.