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Meng, Xiannong
Bucknell University
Lewisburg, Pennsylvania
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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.

Date Reviewed  
- 10 of 53 reviews

  China livestreaming e-commerce industry insights
Si R., Palgrave Macmillan, Cham, Switzerland, 2021. 125 pp.  Type: Book (978-9-811653-43-8)

We know the term “TikTok” refers to a form of short videos. But we might not have related it to commerce. In this 100-page book, Ruo Si vividly introduces readers to the essence of the livestreaming e-commerce indus...

Jun 13 2022  
  Question Answering in Knowledge Bases: A Verification Assisted Model with Iterative Training
Zhang R., Wang Y., Mao Y., Huai J. ACM Transactions on Information Systems 37(4): 1-26, 2019.  Type: Article

Zhang et. al., in their paper, present a novel approach to increase the accuracy and efficiency in question-answering systems over a knowledge base. As they explain, “[mapping] a question in a natural language into a fact tri...

May 10 2022  
   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...

Jan 20 2022  
   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...

Dec 2 2021  
   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...

Feb 17 2021  
  Smart and digital cities: from computational intelligence to applied social sciences
Coelho V., Coelho I., Oliveira T., Ochi L., Springer International Publishing, New York, NY, 2019. 309 pp.  Type: Book (978-3-030122-54-6)

This collection covers a range of topics related to smart and digital cities. The book’s four parts (18 chapters) lead the reader from theories to simulations to social science discussions, all related to the subject. Each ch...

Apr 15 2020  
  Sequential recommendation with user memory networks
Chen X., Xu H., Zhang Y., Tang J., Cao Y., Qin Z., Zha H.  WSDM 2018 (Proceedings of the 11th ACM International Conference on Web Search and Data Mining, Marina Del Rey, CA, Feb 5-9, 2018) 108-116, 2018.  Type: Proceedings

Chen et al. demonstrate a new algorithm that makes recommendation systems more efficient and effective, especially for systems that keep track of user behaviors such as those found on shopping websites. The proposed algorithm makes goo...

Mar 25 2020  
  From urban legends to political fact-checking: online scrutiny in America, 1990-2015
Aspray W., Cortada J., Springer International Publishing, New York, NY, 2019. 146 pp.  Type: Book (978-3-030229-51-1)

In this fascinating five-chapter book, the authors discuss the history of online scrutiny in the US from 1990 to 2015. They review some basic frameworks of online communication; examine the role of online platforms such as Usenet and t...

Jan 21 2020  
  Information diffusion prediction with network regularized role-based user representation learning
Wang Z., Chen C., Li W. ACM Transactions on Knowledge Discovery from Data 13(3): 1-23, 2019.  Type: Article

Wang et al. propose and evaluate the network regularized diffusion representation (NRDR) learning model to tackle the issue of information diffusion prediction. The results show that NRDR works better than other state-of-the-art models...

Sep 12 2019  
  Feature selection and enhanced krill herd algorithm for text document clustering
Abualigah L., Springer International Publishing, New York, NY, 2019. 165 pp.  Type: Book (978-3-030106-73-7)

This monograph, which comes out of the author’s PhD thesis, studies text document clustering with the help of the krill herd (KH) algorithm....

May 10 2019  
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