Simulating the effectiveness of using association rules for recommendation systems

Authors:
Jonghoon Chun;Jae Young Oh;Sedong Kwon;Dongkyu Kim
Affiliations:
President & CEO, Corelogix Inc, Department of Computer Engineering, Myongji University, Yongin, Kyunggi-Do, Korea;Department of Computer Engineering, Myongji University, Yongin, Kyunggi-Do, Korea;Department of Computer Engineering, Myongji University, Yongin, Kyunggi-Do, Korea;R&D Director & CTO, Corelogix Inc, Seoul, Korea
Venue:
AsiaSim'04 Proceedings of the Third Asian simulation conference on Systems Modeling and Simulation: theory and applications
Year:
2004

Citing 7
Cited 3

Using collaborative filtering to weave an information tapestry

Communications of the ACM - Special issue on information filtering
Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
GroupLens: an open architecture for collaborative filtering of netnews

CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”

CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Real world performance of association rule algorithms

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases

Mining Indirect Association Rules for Web Recommendation

International Journal of Applied Mathematics and Computer Science
Personalized web recommendation based on path clustering

ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Personalized web recommendation based on path clustering

FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recommendation systems help overcome information overload by providing personalized suggestions based on a history of users' preference. Association rule-based filtering method is often used for automatic recommendation systems yet it inherently lacks ability to single out a product to recommend for each individual user. In this paper, we propose an association rule ranking algorithm. In the algorithm, we measure how much a user is relevant to every association rule by comparing attributes of a user with the attributes of others who belong to the same association rule. By providing such an algorithm, it is possible to recommend products with associated rankings, which results in better customer satisfaction. We show through simulations, that the accuracy of association rule-based filtering is improved if we appropriately rank association rules for a given user.