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Authors: Michael Meisel ; Maik Benndorf and Andreas Ittner

Affiliation: Hochschule Mittweida, Germany

ISBN: 978-989-8565-75-4

Keyword(s): Data Mining in Electronic Commerce, Mining Text and Semi-structured Data.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining in Electronic Commerce ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Mining Text and Semi-Structured Data ; Symbolic Systems

Abstract: Many search engine marketing campaigns contain a lot of different search queries with a low frequency referred as "Long Tail". It is not possible to draw reliable conclusions about the performance of a specific search query with low frequency regarding a business goal because of its limited sample size. In this paper we present a method for finding profitable patterns in the long tail of search queries. The method aggregates search queries based on mined patterns and rejects the non profitable groups. We applied our method to a search engine marketing campaign with over 10,000 different search queries and performed an offline test and an online A/B-test to measure the performance of the method.

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Paper citation in several formats:
Meisel M., Benndorf M. and Ittner A. (2013). Mining the Long Tail of Search Queries - Finding Profitable Patterns.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: SSTM, (IC3K 2013) ISBN 978-989-8565-75-4, pages 225-229. DOI: 10.5220/0004521602250229

@conference{sstm13,
author={Michael Meisel and Maik Benndorf and Andreas Ittner},
title={Mining the Long Tail of Search Queries - Finding Profitable Patterns},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: SSTM, (IC3K 2013)},
year={2013},
pages={225-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004521602250229},
isbn={978-989-8565-75-4},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: SSTM, (IC3K 2013)
TI - Mining the Long Tail of Search Queries - Finding Profitable Patterns
SN - 978-989-8565-75-4
AU - Meisel M.
AU - Benndorf M.
AU - Ittner A.
PY - 2013
SP - 225
EP - 229
DO - 10.5220/0004521602250229

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