Semantic search in business news - a notebook article
Semantic search is revolutionizing how we discover and consume news articles, offering a more intuitive and efficient method for finding relevant content and curating personalized news feeds. By embedding the nuances and underlying concepts of text documents in vectors, we can retrieve articles that align closely with the user's interests, preferences, and browsing history.
Superlinked is designed to handle these challenges, empowering you to scale efficiently and - using Superlinked Spaces - prioritize semantic relevance and/or recency so you can recommend highly relevant news articles to your users without having to re-embed your dataset.
To illustrate, we'll take you step by step through building a semantic-search-powered business news recommendation app, using the following parts of Superlinked's library:
Using these spaces to embed our articles' headlines, text, and publication dates, we'll be able to skew our results towards older or more recent news as desired, and also search using specific search terms or a specific news article.
