querymodellen

Querymodellen refers to the set of representations and methods used to describe and interpret user queries within information retrieval systems. The goal is to infer user intent and produce relevance estimates that rank documents according to how well they satisfy the query.

Historically, query models have been probabilistic or lexical. A common framework is the language-model approach, where a query is treated as a short text and documents are scored by the likelihood of the query given the document or by a posterior probability P(D|Q). Classical models include the query likelihood model and BM25, which combines term frequency, document length, and inverse document frequency to compute relevance.

Modern developments incorporate semantic representations. Neural ranking and embedding-based models map queries and documents into continuous vector spaces, enabling semantic matching and context-aware retrieval. Techniques such as query expansion, pseudo-relevance feedback, and personalization use user history to refine the query model.

Applications range from web search and digital libraries to e-commerce search and question answering. Evaluation relies on standard information-retrieval metrics such as precision, recall, NDCG, and MAP, using benchmark datasets. Challenges include short, ambiguous queries, evolving user intent, multilingual content, and the need for scalable, privacy-preserving systems.