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| Xiannong Meng is a professor of computer science (CS) at Bucknell University. He received his PhD in CS from Worcester Polytechnic Institute in 1990, and taught at the University of Texas–Pan American (now the University of Texas Rio Grande Valley) from 1994 to 2001. He joined Bucknell in 2001. His research and teaching interests include information retrieval, distributed computing, intelligent web search, operating systems, computer networks, and CS education. His PhD research focused on performance measurement in computer networks with multiple classes of traffic, now known as “multimedia networks.” The work involved investigating the performance of network architectures and protocols that support multimedia, using measurement, simulation, and queueing models as tools. Later on, in the late 1990s, Xiannong and colleagues worked on intelligent web search when they built some small-scale search engines that employ relevance feedback technologies, which allow users to search the web interactively. More specifically, the user enters a search query and the search engine returns an initial set of results based on the query. The user can mark the top results as relevant or irrelevant before sending the feedback to the search engine. The search engine, based on this feedback, refines the search and generates a new set of results. This process can continue at the user’s preference. Xiannong is also interested in how to effectively teach the subject of information retrieval at the undergraduate level. He successfully offered the first web information retrieval course at Bucknell in early 2000. The course combined computer network components and information retrieval, and students were asked to build a search engine using a high-level programming language and term frequency–inverse document frequency as the basic search strategy. He continues to research text search that can be used in many different application areas. Xiannong and colleagues recently investigated the general topic of undergraduate CS curricula in both the US and China. They published some initial results from the comparison in the 2019 ACM Conference on Global Computing Education. Xiannong has been a reviewer for Computing Reviews since 2009. |
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Incorporating System-Level Objectives into Recommender Systems Abdollahpouri H. (Companion Proceedings of The 2019 World Wide Web Conference, San Francisco, USA, May 13-17, 2019) 2-6, 2019. Type: Proceedings This paper proposes and evaluates two algorithms for recommendation systems. Most recommendation systems concentrate on optimizing one primary metric, for example, the advantage of a consumer purchasing an item or minimizing the cost of operations...
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Jan 20 2022 |
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A survey on session-based recommender systems Wang S., Cao L., Wang Y., Sheng Q., Orgun M., Lian D. ACM Computing Surveys 7(54): 1-38, 2022. Type: Article
Wang et al. present a comprehensive survey with this paper. A session-based recommender system (SBRS) is a system that makes recommendations to users based on short-term, dynamic user preferences (in a session). It is different from ot...
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Dec 2 2021 |
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The practice of crowdsourcing Alonso O., Morgan&Claypool Publishers, San Rafael, CA, 2019. 150 pp. Type: Book (978-1-681735-23-8)
The practice of crowdsourcing provides a very concise, yet very practical, guide to crowdsourcing computing. In computer science, we discuss algorithms, software, hardware, design, and societal issues in computing, among many ot...
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Feb 17 2021 |
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Text data management and analysis: a practical introduction to information retrieval and text mining Zhai C., Massung S., Association for Computing Machinery and Morgan & Claypool, New York, NY, 2016. 530 pp. Type: Book, Reviews: (4 of 4)
Zhai and Massung’s new book Text data management and analysis provides a fresh new look at the areas of text retrieval, text mining, and text management. Traditionally, these three areas are separate, each with a rich ...
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Nov 14 2016 |
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An improved clustering ensemble method based link analysis Hao Z., Wang L., Cai R., Wen W. World Wide Web 18(2): 185-195, 2015. Type: Article
A method to improve clustering ensembles of datasets, called WETU, is presented in this paper. The current clustering ensemble methods use measurements, such as the weighted connection-triple (WCT), the weighted triple-quality (WTQ), a...
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May 27 2015 |
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