Computational models of information scent-following in a very large browsable text collection
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Web Structure, Dynamics and Page Quality
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Evolution of the Chilean Web Structure Composition
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Web Dynamics
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Variable latent semantic indexing
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
User modeling for full-text federated search in peer-to-peer networks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Mining search and browse logs for web search: A Survey
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Given the rate of growth of the Web, scalability of search engines is a key issue, as the amount of hardware and network resources needed is large, and expensive. In addition, search engines are popular tools, so they have heavy constraints on query answer time. So, the efficient use of resources can improve both scalability and answer time. One tool to achieve these goals is Web mining. Web mining has three branches: link mining, usage mining, and content mining. One important analysis in all these cases is the dynamic behavior. Here we give examples of link and usage mining related to search engines, as well as the related Web dynamics.