Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Home Topics Titles Quotes Blog Featured Help
Search
 
Xiannong Meng
Bucknell University
Lewisburg, Pennsylvania
 

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.


     

 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...

 

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...

 

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...

 

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...

 

 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...

 
  more...

 
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 2004 Reviews.com™
Terms of Use
| Privacy Policy