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.
%0 Conference Paper
%1 Lappas:2012:EDC:2339530.2339599
%A Lappas, Theodoros
%A Valkanas, George
%A Gunopulos, Dimitrios
%B Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
%C New York, NY, USA
%D 2012
%I ACM
%K competitor mining recommendation
%P 408--416
%R 10.1145/2339530.2339599
%T Efficient and domain-invariant competitor mining
%U http://doi.acm.org/10.1145/2339530.2339599
%X 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.
%@ 978-1-4503-1462-6
@inproceedings{Lappas:2012:EDC:2339530.2339599,
abstract = {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.},
acmid = {2339599},
added-at = {2013-08-22T17:14:41.000+0200},
address = {New York, NY, USA},
author = {Lappas, Theodoros and Valkanas, George and Gunopulos, Dimitrios},
biburl = {https://www.bibsonomy.org/bibtex/24628b5be423017fe42c0a4795f0c16e6/schwemmlein},
booktitle = {Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining},
description = {Efficient and domain-invariant competitor mining},
doi = {10.1145/2339530.2339599},
interhash = {ebba8cf6f3825bf6450a411a9a1b1c97},
intrahash = {4628b5be423017fe42c0a4795f0c16e6},
isbn = {978-1-4503-1462-6},
keywords = {competitor mining recommendation},
location = {Beijing, China},
numpages = {9},
pages = {408--416},
publisher = {ACM},
series = {KDD '12},
timestamp = {2013-08-22T17:14:41.000+0200},
title = {Efficient and domain-invariant competitor mining},
url = {http://doi.acm.org/10.1145/2339530.2339599},
year = 2012
}