Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Data mining: concepts and techniques
Data mining: concepts and techniques
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Analysis of large data logs: an application of Poisson sampling on excite web queries
Information Processing and Management: an International Journal
Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Using information retrieval techniques for supporting data mining
Data & Knowledge Engineering
Efficient mining of group patterns from user movement data
Data & Knowledge Engineering
Personalized mining of web documents using link structures and fuzzy concept networks
Applied Soft Computing
Association rules mining using heavy itemsets
Data & Knowledge Engineering
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
Efficient mining of generalized association rules with non-uniform minimum support
Data & Knowledge Engineering
Mining association rules with multiple minimum supports using maximum constraints
International Journal of Approximate Reasoning
CBAR: an efficient method for mining association rules
Knowledge-Based Systems
Parallel TID-based frequent pattern mining algorithm on a PC Cluster and grid computing system
Expert Systems with Applications: An International Journal
Web usage mining for improving students performance in learning management systems
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Expert Systems with Applications: An International Journal
Mining association rules to support resource allocation in business process management
Expert Systems with Applications: An International Journal
Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems
Expert Systems with Applications: An International Journal
Web usage mining to improve the design of an e-commerce website: OrOliveSur.com
Expert Systems with Applications: An International Journal
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper a new method towards automatic personalized recommendation based on the behavior of a single user in accordance with all other users in web-based information systems is introduced. The proposal applies a modified version of the well-known Apriori data mining algorithm to the log files of a web site (primarily, an e-commerce or an e-learning site) to help the users to the selection of the best user-tailored links. The paper mainly analyzes the process of discovering association rules in this kind of big repositories and of transforming them into user-adapted recommendations by the two-step modified Apriori technique, which may be described as follows. A first pass of the modified Apriori algorithm verifies the existence of association rules in order to obtain a new repository of transactions that reflect the observed rules. A second pass of the proposed Apriori mechanism aims in discovering the rules that are really inter-associated. This way the behavior of a user is not determined by ''what he does'' but by ''how he does''. Furthermore, an efficient implementation has been performed to obtain results in real-time. As soon as a user closes his session in the web system, all data are recalculated to take the recent interaction into account for the next recommendations. Early results have shown that it is possible to run this model in web sites of medium size.