PPT-A Formal Study of Information Retrieval Heuristics

Hui Fang Tao Tao ChengXiang Zhai University of Illinois at Urbana Champaign SIGIR 2004 Best Paper Presented by Lingjie Zhang Outline Formal Definitions of Heuristic Retrieval Constraints. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Retrieval Models. Model is an idealization or abstraction of an actual process. in this case, process is matching of documents with queries, i.e., retrieval. 1. . HEURISTICS FOR PROCESS SYNTHESIS. Ref:. . Seider. , Seader and Lewin (2004), Chapter 5. Heuristics . 2. Introduction. Chemical reaction (to eliminate differences in molecular type). Mixing and recycle (to distribute the chemicals). By. Farnoosh Davoodi. 1. Agenda. Min Knapsack Problem. 2 approximation greedy algorithm. Proof . 3/2 approximation greedy algorithm. Proof. Another improved heuristic. Heuristics for the O-1 Min-Knapsack Problem. INST 734. Module 3. Doug . Oard. Agenda. Ranked retrieval. Similarity-based ranking. Probability-based ranking. Boolean Retrieval. Strong points. Accurate, . if you know the right strategies. Efficient for the computer. 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 . Date :. . 2012 . / . 04. . / . 12. 資訊碩一 . 10077034. 蔡勇儀 . @. . LAB603 . Outline. Introduction. Preliminaries. Method. Experimental result. Conclusions. Introduction. Image retrieval have more challenge than text retrieval.. Hongning Wang. Congratulations. Job . Offer from Bing Core Ranking team. Design the ranking module for Bing.com. CS 6501: Information Retrieval. 2. CS@UVa. How should I rank documents?. Answer: Rank by relevance!. Hongning Wang. Congratulations. Job . Offer from Bing Core Ranking team. Design the ranking module for Bing.com. CS 4501: Information Retrieval. 2. CS@UVa. How should I rank documents?. Answer: Rank by relevance!. Hongning. Wang. CS@UVa. What is information retrieval?. CS6501: Information Retrieval. CS@UVa. 2. Why information retrieval . Information overload. “. It refers to the . difficulty. a person can have understanding an issue and making decisions that can be caused by the presence of . BY. DR. ADNAN ABID. Lecture # . Introduction. Library Management System. Structured Data Storage / Tables. Semi-Structured and Unstructured . Employee Department Salary. Library Digitization. Information Retrieval Models. 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 --. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . Many heuristics have been developed to address some particular issues related to user interface designs. For example, Pinelle et al. [11] had developed a set of ten heuristics for video game design. T Rosalia F. Tungaraza. Advisor: Prof. Linda G. Shapiro. Ph.D. Defense. Computer Science & Engineering. University of Washington. 1. Functional Brain Imaging. Study how the brain works . Imaging while subject performs a task .