Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Using Markov models for web site link prediction
Proceedings of the thirteenth ACM conference on Hypertext and hypermedia
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Relational Markov models and their application to adaptive web navigation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Creating Adaptive Web Sites Through Usage-Based Clustering of URLs
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Using Sequential and Non-Sequential Patterns in Predictive Web Usage Mining Tasks
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
REFEREE: an open framework for practical testing of recommender systems using ResearchIndex
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Exploiting probabilistic latent information for the construction of community web directories
UM'05 Proceedings of the 10th international conference on User Modeling
USER: user-sensitive expert recommendations for knowledge-dense environments
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
WebKDD 2006: web mining and web usage analysis post-workshop report
ACM SIGKDD Explorations Newsletter
A hybrid web recommender system based on Q-learning
Proceedings of the 2008 ACM symposium on Applied computing
Personalized cluster-based semantically enriched web search for e-learning
Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web
Expert Systems with Applications: An International Journal
Combination of Web page recommender systems
Expert Systems with Applications: An International Journal
Incorporating usage information into average-clicks algorithm
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Predictive web automation assistant for people with vision impairments
Proceedings of the 22nd international conference on World Wide Web
Mining taxonomies from web menus: rule-based concepts and algorithms
ICWE'13 Proceedings of the 13th international conference on Web Engineering
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Recent studies have shown that conceptual and structural characteristics of a website can play an important role in the quality of recommendations provided by a recommendation system. Resources like Google Directory, Yahoo! Directory and web-content management systems attempt to organize content conceptually. Most recommendation models are limited in their ability to use this domain knowledge. We propose a novel technique to incorporate the conceptual characteristics of a website into a usage-based recommendation model. We use a framework based on biological sequence alignment. Similarity scores play a crucial role in such a construction and we introduce a scoring system that is generated from the website's concept hierarchy. These scores fit seamlessly with other quantities used in similarity calculation like browsing order and time spent on a page. Additionally they demonstrate a simple, extensible system for assimilating more domain knowledge. We provide experimental results to illustrate the benefits of using concept hierarchy.