Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Iterative solution methods
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 11th international conference on World Wide Web
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 1 - Volume 1
Efficient pagerank approximation via graph aggregation
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
A framework for ontology-driven subspace clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A MFoM learning approach to robust multiclass multi-label text categorization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using a Layered Markov Model for Distributed Web Ranking Computation
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Exploiting the hierarchical structure for link analysis
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Generalizing PageRank: damping functions for link-based ranking algorithms
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Constructing informative prior distributions from domain knowledge in text classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge transformation from word space to document space
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Co-ranking Authors and Documents in a Heterogeneous Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Exploiting data preparation to enhance mining and knowledgediscovery
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Discovering golden nuggets: data mining in financial application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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To gain competitiveness and sustained growth in the 21st century, most businesses are on a mission to become more customer-centric. In order to succeed in this endeavor, it is crucial not only to synthesize and analyze the VOC (the VOice of the Customer) data (i.e., the feedbacks or requirements raised by customers), but also to quickly turn these data into actionable knowledge. Although there are many technologies being developed in this complex problem space, most existing approaches in analyzing customer requests are ad hoc, time-consuming, error-prone, people-based processes which hardly scale well as the quantity of customer information explodes. This often results in the slow response to customer requests. In this article, in order to mine VOC to extract useful knowledge for the best product or service quality, we develop a hybrid framework that integrates domain knowledge with data-driven approaches to analyze the semi-structured customer requests. The framework consists of capturing functional features, discovering the overlap or correlation among the features, and identifying the evolving feature trend by using the knowledge transformation model. In addition, since understanding the relative importance of the individual customer request is very critical and has a direct impact on the effective prioritization in the development process, we develop a novel semantic enhanced link-based ranking (SELRank) algorithm for relatively rating/ranking both customer requests and products. The framework has been successfully applied on Xerox Office Group Feature Enhancement Requirements (XOG FER) datasets to analyze customer requests.