Search For:

Displaying 1-24 out of 24 total
Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization
Found in: IEEE Transactions on Services Computing
By Zibin Zheng,Hao Ma,Michael R. Lyu,Irwin King
Issue Date:July 2013
pp. 289-299
With the increasing presence and adoption of web services on the World Wide Web, the demand of efficient web service quality evaluation approaches is becoming unprecedentedly strong. To avoid the expensive and time-consuming web service invocations, this p...
 
Mining test oracles of web search engines
Found in: Automated Software Engineering, International Conference on
By Wujie Zheng,Hao Ma,Michael R. Lyu,Tao Xie,Irwin King
Issue Date:November 2011
pp. 408-411
Web search engines have major impact in people's everyday life. It is of great importance to test the retrieval effectiveness of search engines. However, it is labor-intensive to judge the relevance of search results for a large number of queries, and thes...
 
Mining Web Graphs for Recommendations
Found in: IEEE Transactions on Knowledge and Data Engineering
By Hao Ma,Irwin King,Michael Rung-Tsong Lyu
Issue Date:June 2012
pp. 1051-1064
As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including movies, music, images, books re...
 
QoS-Aware Web Service Recommendation by Collaborative Filtering
Found in: IEEE Transactions on Services Computing
By Zibin Zheng, Hao Ma, Michael R. Lyu, Irwin King
Issue Date:April 2011
pp. 140-152
With increasing presence and adoption of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming important for describing nonfunctional characteristics of Web services. In this paper, we present a collaborative filtering approach for predi...
 
The Detection of Range Extended Target Based on Adaptive Searching and Twofold Sliding Windows
Found in: Electrical and Control Engineering, International Conference on
By Xu Hao, Ma Yunfei, Jiang Tao, Wang Dongjin, Chen Weidong
Issue Date:June 2010
pp. 4803-4806
The detection of range extended target in UWB radar has been the research focus in the field of signal detection. The paper gives a detection method based on twofold sliding windows, and the theoretical value of the detection threshold using adaptive searc...
 
TagRec: Leveraging Tagging Wisdom for Recommendation
Found in: Computational Science and Engineering, IEEE International Conference on
By Tom Chao Zhou, Hao Ma, Irwin King, Michael R. Lyu
Issue Date:August 2009
pp. 194-199
Due to the exponential growth of information on the Web, Recommender Systems have been developed to generate suggestions to help users overcome information overload and sift through huge amounts of information efficiently. Many existing approaches to recom...
 
WSRec: A Collaborative Filtering Based Web Service Recommender System
Found in: Web Services, IEEE International Conference on
By Zibin Zheng, Hao Ma, Michael R. Lyu, Irwin King
Issue Date:July 2009
pp. 437-444
As the abundance of Web services on the World Wide Web increase,designing effective approaches for Web service selection and recommendation has become more and more important. In this paper, we present WSRec, a Web service recommender system, to attack thi...
 
SCTP-Based Server Cluster Heartbeat Detection Mechanism
Found in: Computer Science and Computational Technology, International Symposium on
By Zhang Lei, Dai Hao, Ma Mingkai, Xu Shaoqing
Issue Date:December 2008
pp. 118-122
The server cluster technology become more and more prevalent in the enterprise level applications, failure detection is one of the key technologies which can ensure the server cluster continue providing services. This paper introduced several types of the ...
 
Using data mining to discover the correlation between web learning portfolios and achievements
Found in: Frontiers in Education, Annual
By Chien-Ming Chen, Cheng-Hao Ma, Bin-Shyan Jong, Yen-Teh Hsia, Tsong-Wuu Lin
Issue Date:October 2008
pp. F2F-9-F2F-14
Internet learning is different from traditional classroom teaching. This is because there is no actual contact between teachers and students; therefore, it is difficult for teachers to keep track of studentspsila learning conditions. By analyzing the learn...
 
Crawling the eDonkey Network
Found in: Grid and Cooperative Computing Workshops, International Conference on
By Jia Yang, Hao Ma, Weijia Song, Jian Cui, Changling Zhou
Issue Date:October 2006
pp. 133-136
In this paper we introduce an active-probing approach that measures the eDonkey (eDonkey 2000) network by us- ing an experimental client developed for this purpose. Our approach collects information from the eDonkey network by issuing queries for files. Ba...
 
An experimental study on implicit social recommendation
Found in: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR '13)
By Hao Ma
Issue Date:July 2013
pp. 73-82
Social recommendation problems have drawn a lot of attention recently due to the prevalence of social networking sites. The experiments in previous literature suggest that social information is very effective in improving traditional recommendation algorit...
     
Probabilistic factor models for web site recommendation
Found in: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information (SIGIR '11)
By Chao Liu, Hao Ma, Irwin King, Michael R. Lyu
Issue Date:July 2011
pp. 265-274
Due to the prevalence of personalization and information filtering applications, modeling users' interests on the Web has become increasingly important during the past few years. In this paper, aiming at providing accurate personalized Web site recommendat...
     
Improving Recommender Systems by Incorporating Social Contextual Information
Found in: ACM Transactions on Information Systems (TOIS)
By Hao Ma, Irwin King, Michael R. Lyu, Tom Chao Zhou
Issue Date:April 2011
pp. 1-23
Due to their potential commercial value and the associated great research challenges, recommender systems have been extensively studied by both academia and industry recently. However, the data sparsity problem of the involved user-item matrix seriously af...
     
Learning to recommend with explicit and implicit social relations
Found in: ACM Transactions on Intelligent Systems and Technology (TIST)
By Hao Ma, Irwin King, Michael R. Lyu
Issue Date:April 2011
pp. 1-19
Recommender systems have been well studied and developed, both in academia and in industry recently. However, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections amo...
     
Semi-nonnegative matrix factorization with global statistical consistency for collaborative filtering
Found in: Proceeding of the 18th ACM conference on Information and knowledge management (CIKM '09)
By Haixuan Yang, Hao Ma, Irwin King, Michael R. Lyu
Issue Date:November 2009
pp. 767-776
Collaborative Filtering, considered by many researchers as the most important technique for information filtering, has been extensively studied by both academic and industrial communities. One of the most popular approaches to collaborative filtering recom...
     
Improving search engines using human computation games
Found in: Proceeding of the 18th ACM conference on Information and knowledge management (CIKM '09)
By Abhishek Gupta, Chris Quirk, Hao Ma, Raman Chandrasekar
Issue Date:November 2009
pp. 275-284
Work on evaluating and improving the relevance of web search engines typically use human relevance judgments or clickthrough data. Both these methods look at the problem of learning the mapping from queries to web pages. In this paper, we identify some iss...
     
Learning to recommend with trust and distrust relationships
Found in: Proceedings of the third ACM conference on Recommender systems (RecSys '09)
By Hao Ma, Irwin King, Michael R. Lyu
Issue Date:October 2009
pp. 189-196
With the exponential growth of Web contents, Recommender System has become indispensable for discovering new information that might interest Web users. Despite their success in the industry, traditional recommender systems suffer from several problems. Fir...
     
Page hunt: improving search engines using human computation games
Found in: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '09)
By Abhishek Gupta, Chris Quirk, Hao Ma, Raman Chandrasekar
Issue Date:July 2009
pp. 435-435
There has been a lot of work on evaluating and improving the relevance of web search engines. In this paper, we suggest using human computation games to elicit data from players that can be used to improve search. We describe Page Hunt, a single-player gam...
     
Learning to recommend with social trust ensemble
Found in: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '09)
By Hao Ma, Irwin King, Michael R. Lyu
Issue Date:July 2009
pp. 435-435
As an indispensable technique in the field of Information Filtering, Recommender System has been well studied and developed both in academia and in industry recently. However, most of current recommender systems suffer the following problems: (1) The large...
     
Page Hunt: using human computation games to improve web search
Found in: Proceedings of the ACM SIGKDD Workshop on Human Computation (HCOMP '09)
By Abhishek Gupta, Chris Quirk, Hao Ma, Raman Chandrasekar
Issue Date:June 2009
pp. 27-28
There has been a lot of work on evaluating and improving the relevance of web search engines, primarily using human relevance judgments or using clickthrough data. Both of these approaches look at the problem of learning the mapping from queries to web pag...
     
SoRec: social recommendation using probabilistic matrix factorization
Found in: Proceeding of the 17th ACM conference on Information and knowledge mining (CIKM '08)
By Haixuan Yang, Hao Ma, Irwin King, Michael R. Lyu
Issue Date:October 2008
pp. 1001-1001
Data sparsity, scalability and prediction quality have been recognized as the three most crucial challenges that every collaborative filtering algorithm or recommender system confronts. Many existing approaches to recommender systems can neither handle ver...
     
Learning latent semantic relations from clickthrough data for query suggestion
Found in: Proceeding of the 17th ACM conference on Information and knowledge mining (CIKM '08)
By Haixuan Yang, Hao Ma, Irwin King, Michael R. Lyu
Issue Date:October 2008
pp. 1001-1001
For a given query raised by a specific user, the Query Suggestion technique aims to recommend relevant queries which potentially suit the information needs of that user. Due to the complexity of the Web structure and the ambiguity of users' inputs, most of...
     
Mining social networks using heat diffusion processes for marketing candidates selection
Found in: Proceeding of the 17th ACM conference on Information and knowledge mining (CIKM '08)
By Haixuan Yang, Hao Ma, Irwin King, Michael R. Lyu
Issue Date:October 2008
pp. 1001-1001
Social Network Marketing techniques employ pre-existing social networks to increase brands or products awareness through word-of-mouth promotion. Full understanding of social network marketing and the potential candidates that can thus be marketed to certa...
     
Effective missing data prediction for collaborative filtering
Found in: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '07)
By Hao Ma, Irwin King, Michael R. Lyu
Issue Date:July 2007
pp. 39-46
Memory-based collaborative filtering algorithms have been widely adopted in many popular recommender systems, although these approaches all suffer from data sparsity and poor prediction quality problems. Usually, the user-item matrix is quite sparse, which...
     
 1