Information retrieval models. Foundations and relationships (Q2841883)

Information retrieval (IR) models are a core component of IR research and IR systems. The past decades brought a consolidation of the family of IR models that consist of relatively isolated views on TF-IDF (term-frequency times inverse-document-frequency) as a weighting scheme in the vector-space model, the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, the best-match version 25 (BM25), the language modelling (LM), and recently the divergence from randomness.NEWLINENEWLINEThis book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks, and divergence-based models. The aim is to create a consolidated and balanced view on the main models. A particular focus of this book is on the relationships between models. This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models.