Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the sixth 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
MPIS: Maximal-Profit Item Selection with Cross-Selling Considerations
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A microeconomic data mining problem: customer-oriented catalog segmentation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
Data Mining for Inventory Item Selection with Cross-Selling Considerations
Data Mining and Knowledge Discovery
Bias and controversy: beyond the statistical deviation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge actionability: satisfying technical and business interestingness
International Journal of Business Intelligence and Data Mining
Catalog segmentation with double constraints in business
Pattern Recognition Letters
Choose the Damping, Choose the Ranking?
WAW '09 Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph
HITS Can Converge Slowly, but Not Too Slowly, in Score and Rank
COCOON '09 Proceedings of the 15th Annual International Conference on Computing and Combinatorics
Mining action rules from scratch
Expert Systems with Applications: An International Journal
Choose the damping, choose the ranking?
Journal of Discrete Algorithms
A new approach of inventory classification based on loss profit
Expert Systems with Applications: An International Journal
DualRank: a dual-phase algorithm for optimal profit mining in retailing market
ASIAN'05 Proceedings of the 10th Asian Computing Science conference on Advances in computer science: data management on the web
Incorporating frequency, recency and profit in sequential pattern based recommender systems
Intelligent Data Analysis
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A fundamental problem in business and other applications is ranking items with respect to some notion of profit based on historical transactions. The difficulty is that the profit of one item not only comes from its own sales, but also from its influence on the sales of other items, i.e., the "cross-selling effect". In this paper, we draw an analogy between this influence and the mutual reinforcement of hub/authority web pages. Based on this analogy, we present a novel approach to the item ranking problem.We apply this ranking approach to solve two selection problems. In size-constrained selection, the maximum number of items that can be selected is fixed. In cost-constrained selection, there is no maximum number of items to be selected, but there is some cost associated with the selection of each item. In both cases, the question is what items should be selected to maximize the profit. Empirically, we show that this method finds profitable items in the presence of cross-selling effect.