PPT-Retrieval Models and Ranking Systems

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 ie retrieval. Chris Manning, Pandu Nayak and . Prabhakar. . Raghavan. Who are these people?. Stephen Robertson. Keith van . Rijsbergen. Karen . Sp. ä. rck. . Jones. Summary – vector space ranking. Represent the query as a weighted tf-idf vector. W. ho’s #1?. Jonathon Peterson. Purdue University. The Ranking Problem. Why is ranking of sports teams important?. College football – BCS. College basketball – NCAA tournament. Win $1 billion!!!. 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. Sathish. . Vadhiyar. List Ranking on GPUs. Linked list prefix computations – computations of prefix sum on the elements contained in a linked list. Irregular memory accesses – successor of each node of a linked list can be contained anywhere. 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. for Information Retrieval. This Talk. Learning to rank for information retrieval. Learning in vector space. Mainly based on papers at SIGIR, WWW, ICML, NIPS, and KDD. Papers at other conferences and journals might not be covered comprehensively.. 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. Group 3. Chad Mills. Esad Suskic. Wee Teck Tan. Outline. System and Data. Document Retrieval. Passage Retrieval. Results. Conclusion. System and Data. Development. Testing. TREC 2004. TREC 2004. TREC 2005. and Re-ranking. Ling573. NLP Systems and Applications. May 3, 2011. Upcoming Talks. Edith Law. Friday: 3:30; CSE 303. Human Computation: Core Research Questions and Opportunities . Games with a purpose, . Through Online Experimentation. WSDM Workshop on Web Search Click Data. February 12. th. , 2012. Yisong Yue. Carnegie Mellon University. Offline Post-hoc Analysis. Launch some ranking function on live traffic. Kai Li, Guo-Jun Qi, Jun Ye, Tuoerhongjiang Yusuph, Kien A. Hua. Department of Computer Science. University of Central Florida. ISM 2016. Presented by . Tuoerhongjiang Yusuph. Introduction. Massive amount of high-dimensional data, high computational costs …. Christopher . Manning and . Pandu . Nayak. Lecture 14: Learning to Rank. Machine learning for IR ranking?. We’ve looked at methods for ranking documents in IR. Cosine similarity, inverse document frequency, . All slides ©Addison Wesley, 2008. Retrieval Models. Provide a mathematical framework for defining the search process. includes explanation of assumptions. basis of many ranking algorithms. can be implicit. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. .