Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Capturing User Access Patterns in the Web for Data Mining
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Sparsity, scalability, and distribution in recommender systems
Sparsity, scalability, and distribution in recommender systems
Mining Web Transaction Patterns in Electronic Commerce Environment
CEC-EAST '04 Proceedings of the E-Commerce Technology for Dynamic E-Business, IEEE International Conference
A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites
IEEE Transactions on Knowledge and Data Engineering
Web Usage Mining Based on Clustering of Browsing Features
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 01
A New Approach for on Line Recommender System in Web Usage Mining
ICACTE '08 Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering
Research on Path Completion Technique in Web Usage Mining
ISCSCT '08 Proceedings of the 2008 International Symposium on Computer Science and Computational Technology - Volume 01
Knowledge Management in E-commerce: A Data Mining Perspective
ICMECG '09 Proceedings of the 2009 International Conference on Management of e-Commerce and e-Government
Pattern Discovery of Web Usage Mining
ICCTD '09 Proceedings of the 2009 International Conference on Computer Technology and Development - Volume 01
Using Incremental Fuzzy Clustering to Web Usage Mining
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
Recommender Systems Handbook
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Information overload is a significant issue of explosive growth of information on the web. The users are facing numerous problems to select and purchase interesting products online. Recommender systems are the software agents, which are helpful to reduce the problem of information overload. In this paper, architectural framework of hybrid recommender system, i.e., semantic enhanced personaliser SEP is proposed for web personalisation. The SEP comprised of three techniques of recommendation such as, original, semantic and category-based recommendation. The original recommendation consists of three components such as user-based collaborative filtering, item-based collaborative filtering and item-based contextual filtering. This recommendation is based on explicit feedback and contextual information provided by the web users while semantic and category-based recommendation is based on implicit feedback using data mining techniques such as, association-rule-mining ARM, similarity measures and clustering. The SEP is capable to solve the problem of scalability, sparsity, quality of recommendation, synonymy, etc.