{"product_id":"9783031012006-information-retrieval-models-foundatio","title":"Information Retrieval Models","description":"\u003cmeta content=\"text\/html; charset=utf-8\" http-equiv=\"Content-Type\"\u003e\u003cp\u003e\u003cspan\u003eFoundations \u0026amp; Relationships\u003cbr\u003eTable of Contents: List of Figures \/ Preface \/ Acknowledgments \/ Introduction \/ Foundations of IR Models \/ Relationships Between IR Models \/ Summary \u0026amp; Research Outlook \/ Bibliography \/ Author's Biography \/ Index\u003cbr\u003eInformation Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF\/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: \"It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works.\" This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-basedmodels. 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. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters. Table of Contents: List of Figures \/ Preface \/ Acknowledgments \/ Introduction \/ Foundations of IR Models \/ Relationships Between IR Models \/ Summary \u0026amp; Research Outlook \/ Bibliography \/ Author's Biography \/ Index\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Rarewaves","offers":[{"title":"Default Title","offer_id":56282745012598,"sku":"9783031012006","price":29.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0092\/7504\/8033\/files\/orig_36198602.jpg?v=1758748786","url":"https:\/\/www.rarewaves.com\/products\/9783031012006-information-retrieval-models-foundatio","provider":"Rarewaves.com","version":"1.0","type":"link"}