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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2018
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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 |
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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. |
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Metawat Kavilkrue Pruet Boonma |
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Metawat Kavilkrue Pruet Boonma Improving efficiency of PromoRank algorithm using dimensionality reduction |
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Metawat Kavilkrue Pruet Boonma |
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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 |
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Improving efficiency of PromoRank algorithm using dimensionality reduction |
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Improving efficiency of PromoRank algorithm using dimensionality reduction |
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improving efficiency of promorank algorithm using dimensionality reduction |
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2018 |
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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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