Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
A taxonomy of DDoS attack and DDoS defense mechanisms
ACM SIGCOMM Computer Communication Review
Lessons and Challenges from Mining Retail E-Commerce Data
Machine Learning
ACM SIGKDD Explorations Newsletter
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Traditional Web usage mining techniques aim at discovering usage patterns from Web data at the page level, while little work is engaged in at some upper level. In this paper, we propose a novel approach to the characterization of Internet users' preference and interests at the domain name level. By summarizing Internet user's domain name access behaviors as the cooccurrences of users and targeting domain names, an aspect model is introduced to classify users and domain names into various groups according to their cooccurrences. Meanwhile, each group is characterized by extracting the property of characteristic users and domain names. Experimental results on real-world data sets show that our approach is effective in which some meaningful groups are identified. Thus, our approach could be used for detecting unusual behaviors on the Internet at the domain name level, which can alleviate the work of searching the joint space of users and domain names.