| 期刊全稱(chēng) | A Feature-Centric View of Information Retrieval | | 影響因子2023 | Donald Metzler | | 視頻video | http://file.papertrans.cn/141/140722/140722.mp4 | | 發(fā)行地址 | Presents a novel paradigm for Web search, which is especially applicable to large data sets.Combines experiences from the author’s academic and industrial research over several years.Delivers the sing | | 學(xué)科分類(lèi) | The Information Retrieval Series | | 圖書(shū)封面 |  | | 影響因子 | .Commercial Web search engines such as Google, Yahoo, and Bing are used every day by millions of people across the globe. With their ever-growing refinement and usage, it has become increasingly difficult for academic researchers to keep up with the collection sizes and other critical research issues related to Web search, which has created a divide between the information retrieval research being done within academia and industry.? Such large collections pose a new set of challenges for information retrieval researchers. .In this work, Metzler describes highly effective information retrieval models for both smaller, classical data sets, and larger Web collections. In a shift away from heuristic, hand-tuned ranking functions and complex probabilistic models, he presents feature-based retrieval models. The Markov random field model he details goes beyond the traditional yet ill-suited bag of words assumption in two ways. First, the model can easily exploit various types of dependencies that exist between query terms, eliminating the term independence assumption that often accompanies bag of words models. Second, arbitrary textual or non-textual features can be used within the model. | | Pindex | Book 2011 |
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