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Efficient and domain-invariant competitor mining

Published: 12 August 2012 Publication History

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.

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Cited By

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  • (2023)When Automatic Filtering Comes to the Rescue: Pre-Computing Company Competitor Pairs in OwlerProceedings of the ACM on Management of Data10.1145/35897871:2(1-23)Online publication date: 20-Jun-2023
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  • (2022)Competitor identificationInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2022.10250765:COnline publication date: 1-Aug-2022
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    cover image ACM Conferences
    KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2012
    1616 pages
    ISBN:9781450314626
    DOI:10.1145/2339530
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 12 August 2012

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    Author Tags

    1. competitor mining
    2. competitors
    3. domain-invariant

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    View all
    • (2023)When Automatic Filtering Comes to the Rescue: Pre-Computing Company Competitor Pairs in OwlerProceedings of the ACM on Management of Data10.1145/35897871:2(1-23)Online publication date: 20-Jun-2023
    • (2022)Competitive Relationship Prediction for Points of Interest: A Neural Graphlet Based ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.306323334:12(5681-5692)Online publication date: 1-Dec-2022
    • (2022)Competitor identificationInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2022.10250765:COnline publication date: 1-Aug-2022
    • (2021)Explainable Recommendation with Comparative Constraints on Product AspectsProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441754(967-975)Online publication date: 8-Mar-2021
    • (2021)Implicit Business Competitor Inference Using Heterogeneous Knowledge Graph2021 IEEE International Conference on Big Knowledge (ICBK)10.1109/ICKG52313.2021.00035(198-205)Online publication date: Dec-2021
    • (2020)Product Supply Optimization for Crowdfunding CampaignsIEEE Transactions on Big Data10.1109/TBDATA.2018.28894796:4(741-756)Online publication date: 1-Dec-2020
    • (2018)Product Adoption Rate Prediction in a Competitive MarketIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.276394430:2(325-338)Online publication date: 1-Feb-2018
    • (2017)Mining Competitors from Large Unstructured DatasetsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.270510129:9(1971-1984)Online publication date: 1-Sep-2017
    • (2017)Comparative Relation Generative ModelIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.264028129:4(771-783)Online publication date: 1-Apr-2017
    • (2017)Company Relation Extraction from Web News Articles for Analyzing Industry Structure2017 IEEE 11th International Conference on Semantic Computing (ICSC)10.1109/ICSC.2017.25(89-92)Online publication date: 2017
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