PPT-CS276: Information Retrieval and Web Search

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Christopher Manning and Pandu Nayak Lecture 14 Learning to Rank Machine learning for IR ranking Weve looked at methods for ranking documents in IR Cosine similarity

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CS276: Information Retrieval and Web Search: Transcript

Christopher Manning and Pandu Nayak Lecture 14 Learning to Rank Machine learning for IR ranking Weve looked at methods for ranking documents in IR Cosine similarity inverse document frequency . Checking in. . By the end of this week you need to have:. Watched the online videos corresponding to the first 6 chapters of . IIR . or/and . read chapters 1–6 of the book. Done programming assignment 1 (due Thursday). By . Rong. Yan, Alexander G. and . Rong. Jin. Mwangi. S. . Kariuki. 2008-11629. Quiz. What’s Negative Pseudo-Relevance feedback in multimedia retrieval?. Introduction. As a result of high demand of content based access to video information.. CSC . 575. Intelligent Information Retrieval. 2. Source: . Intel. How much information?. Google: . ~100 . PB a . day; 3+ million servers (15 . Exabytes. stored). Wayback Machine has . ~9 . PB + . 100 . Miguel Costa. , Daniel Gomes (speaker). Portuguese Web . Archive. Information Retrieval. is the activity of obtaining . information resources relevant. . to an . information need. from a . collection. Information. Miles Efron, Jana . Diesner. , Peter . Organisciak. , Garrick Sherman, Ana . Lucic. {. mefron. , et al.}@. illinois.edu. GSLIS 2012. TREC: The Text REtrieval Conference. NIST. Web. Legal. Course summary. Crista. Lopes. Lecture Objective. Know what you know. Problem Space of this course. “Big Data”. How to. collect it. index it. search it for relevant information. Industry segment . Information Retrieval. Information Retrieval. Konsep. . dasar. . dari. IR . adalah. . pengukuran. . kesamaan. sebuah. . perbandingan. . antara. . dua. . dokumen. , . mengukur. . sebearapa. . ChengXiang. (“Cheng”) . . Zhai. Department of Computer Science. University of Illinois at Urbana-Champaign. http://www.cs.uiuc.edu/homes/czhai. . Email: czhai@illinois.edu. 1. Yahoo!-DAIS Seminar, UIUC. Hongning. Wang. CS@UVa. Classical search engine architecture. “The . Anatomy of a Large-Scale . Hypertextual. Web Search . Engine”. - Sergey . Brin. and . Lawrence Page, . Computer networks and ISDN systems. Hongning. Wang. CS@UVa. CS@UVa. CS6501: Information Retrieval. 1. Abstraction of search engine architecture. User. Ranker. Indexer. Doc Analyzer. Index. results. Crawler. Doc . Representation . Query Rep. What is IR?. Sit down before fact as a little child, . be prepared to give up every conceived notion, . follow humbly wherever and whatever abysses nature leads, . or you will learn nothing. . . -- Thomas Huxley --. Web Science Course. 2. What to Expect. Information Retrieval Basics. IR Systems. History of IR. Retrieval Models. Vector Space Model. Information Retrieval on the Web. Differences to traditional IR. Selected Papers. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . Universität des Saarlandes, Saarbrücken. Winter Semester 2011/12. The Course. Lecturers. Teaching Assistants. October 18, 2011. I.. 2. IR&DM, WS'11/12. D5: Databases & Information Systems Group .

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