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
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 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
Constrained frequent pattern mining: a pattern-growth view
ACM SIGKDD Explorations Newsletter
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
IEEE Transactions on Knowledge and Data Engineering
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
A Rule-Based Recommender System for Online Discussion Forums
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Proceedings of the 14th International Conference on Extending Database Technology
Neighborhood-restricted mining and weighted application of association rules for recommenders
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Mining rare association rules in the datasets with widely varying items' frequencies
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Using association rules to solve the cold-start problem in recommender systems
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Association rule mining is an actively studied topic in recommender systems. A major limitation of an association rule-based recommender system is the problem of reduced coverage. It is generally caused due to the usage of a single global minimum support (minsup) threshold in the mining process, which leads to the effect that no association rules involving rare items can be found. To confront the problem, researchers have introduced Neighborhood-Restricted rule-based Recommender System (NRRS) using the concept of multiple minsups. We have observed that NRRS is computationally expensive to use and can recommend uninteresting products to the users. With this motivation, this paper proposes an improved NRRS using the relative support measure. We call the proposed system as NRRS++. Experimental results show that NRRS++ can provide better recommendations and is runtime efficient than NRRS.