| 期刊全稱(chēng) | A Generative Theory of Relevance | | 影響因子2023 | Victor Lavrenko | | 視頻video | http://file.papertrans.cn/141/140856/140856.mp4 | | 發(fā)行地址 | Includes supplementary material: | | 學(xué)科分類(lèi) | The Information Retrieval Series | | 圖書(shū)封面 |  | | 影響因子 | .A modern information retrieval system must have the capability to find, organize and present very different manifestations of information – such as text, pictures, videos or database records – any of which may be of relevance to the user. However, the concept of relevance, while seemingly intuitive, is actually hard to define, and it‘s even harder to model in a formal way...Lavrenko does not attempt to bring forth a new definition of relevance, nor provide arguments as to why any particular definition might be theoretically superior or more complete. Instead, he takes a widely accepted, albeit somewhat conservative definition, makes several assumptions, and from them develops a new probabilistic model that explicitly captures that notion of relevance. With this book, he makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism; second, a new method for modeling exchangeable sequences of discrete random variables which does not | | Pindex | Book 2009 |
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