Kyselyvektorin

Kyselyvektorin is a Finnish term that translates to "query vector" in English. In the context of information retrieval and search engines, a query vector represents a user's search request as a numerical vector in a multidimensional space. This vectorization process allows for mathematical operations to be performed on search queries, enabling more sophisticated matching and ranking of documents.

The creation of a query vector typically involves techniques from natural language processing and machine learning. Words, phrases, or even entire sentences from a user's query are converted into numerical representations. These representations can be based on word embeddings, such as Word2Vec or GloVe, which capture semantic relationships between words, or on TF-IDF (Term Frequency-Inverse Document Frequency) scores, which reflect the importance of a term within a document and across a corpus.

Once a query is represented as a vector, it can be compared to document vectors using various similarity measures, such as cosine similarity or Euclidean distance. Documents whose vectors are closer to the query vector in the multidimensional space are considered more relevant to the search. This vector-based approach is fundamental to modern search algorithms, facilitating efficient and accurate retrieval of information from large datasets. It also forms the basis for many advanced search functionalities, including semantic search and personalized search results.