Building a Blog Search Engine from Scratch with Word2Vec

The authors built a blog search engine from scratch using Python and Word2Vec embeddings. Posts and search queries are embedded into a 300-dimensional vector space, and cosine similarity is used to rank results. To make it web-friendly, the Word2Vec model is split into an index and vectors, with HTTP Range requests used to download only necessary data, reducing web load significantly. An evaluation metric is designed to assess the search engine's accuracy, and future improvements, such as using TF-IDF to reduce noise, are discussed.