Improving efficiency of PromoRank algorithm using dimensionality reduction

Promotion plays a crucial role in online marketing, which can be used in post-sale recommendation, developing brand, customer support, etc. It is often desirable to find markets or sale channels where an object, e.g., a product, person or service, can be promoted efficiently. Since the object may no...

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Main Authors: Metawat Kavilkrue, Pruet Boonma
Format: Book Series
Published: 2018
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84899925325&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45410
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-454102018-01-24T06:09:58Z Improving efficiency of PromoRank algorithm using dimensionality reduction Metawat Kavilkrue Pruet Boonma Promotion plays a crucial role in online marketing, which can be used in post-sale recommendation, developing brand, customer support, etc. It is often desirable to find markets or sale channels where an object, e.g., a product, person or service, can be promoted efficiently. Since the object may not be highly ranked in the global property space, PromoRank algorithm promotes a given object by discovering promotive subspace in which the target is top rank. However, the computation complexity of PromoRank is exponential to the dimension of the space. This paper proposes to use dimensionality reduction algorithms, such as PCA, in order to reduce the dimension size and, as a consequence, improve the performance of PromoRank. Evaluation results show that the dimensionality reduction algorithm can reduce the execution time of PromoRank up to 25% in large data sets while the ranking result is mostly maintained. © 2014 Springer International Publishing Switzerland. 2018-01-24T06:09:58Z 2018-01-24T06:09:58Z 2014-01-01 Book Series 16113349 03029743 2-s2.0-84899925325 10.1007/978-3-319-05476-6_27 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84899925325&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45410
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description Promotion plays a crucial role in online marketing, which can be used in post-sale recommendation, developing brand, customer support, etc. It is often desirable to find markets or sale channels where an object, e.g., a product, person or service, can be promoted efficiently. Since the object may not be highly ranked in the global property space, PromoRank algorithm promotes a given object by discovering promotive subspace in which the target is top rank. However, the computation complexity of PromoRank is exponential to the dimension of the space. This paper proposes to use dimensionality reduction algorithms, such as PCA, in order to reduce the dimension size and, as a consequence, improve the performance of PromoRank. Evaluation results show that the dimensionality reduction algorithm can reduce the execution time of PromoRank up to 25% in large data sets while the ranking result is mostly maintained. © 2014 Springer International Publishing Switzerland.
format Book Series
author Metawat Kavilkrue
Pruet Boonma
spellingShingle Metawat Kavilkrue
Pruet Boonma
Improving efficiency of PromoRank algorithm using dimensionality reduction
author_facet Metawat Kavilkrue
Pruet Boonma
author_sort Metawat Kavilkrue
title Improving efficiency of PromoRank algorithm using dimensionality reduction
title_short Improving efficiency of PromoRank algorithm using dimensionality reduction
title_full Improving efficiency of PromoRank algorithm using dimensionality reduction
title_fullStr Improving efficiency of PromoRank algorithm using dimensionality reduction
title_full_unstemmed Improving efficiency of PromoRank algorithm using dimensionality reduction
title_sort improving efficiency of promorank algorithm using dimensionality reduction
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84899925325&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45410
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