On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
CoMiner: An Effective Algorithm for Mining Competitors from the Web
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Competitor Mining with the Web
IEEE Transactions on Knowledge and Data Engineering
On domination game analysis for microeconomic data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Promotion analysis in multi-dimensional space
Proceedings of the VLDB Endowment
Region-based online promotion analysis
Proceedings of the 13th International Conference on Extending Database Technology
Identifying the most influential data objects with reverse top-k queries
Proceedings of the VLDB Endowment
Mining comparative opinions from customer reviews for Competitive Intelligence
Decision Support Systems
Pareto-Based Dominant Graph: An Efficient Indexing Structure to Answer Top-K Queries
IEEE Transactions on Knowledge and Data Engineering
Finding top-k profitable products
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Mining competitor relationships from online news: A network-based approach
Electronic Commerce Research and Applications
Web scale competitor discovery using mutual information
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
SkyDiver: a framework for skyline diversification
Proceedings of the 16th International Conference on Extending Database Technology
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In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness relationship between two items? Who are the true competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items. We present efficient methods for evaluating competitiveness in large datasets and address the natural problem of finding the top-k competitors of a given item. Our methodology is evaluated against strong baselines via a user study and experiments on multiple datasets from different domains.