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: | , |
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| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
Springer Verlag
2014
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| 在線閱讀: | http://www.scopus.com/inward/record.url?eid=2-s2.0-84899925325&partnerID=40&md5=187acb993e2720863ba1c7e37064fc14 http://cmuir.cmu.ac.th/handle/6653943832/1256 |
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| 機構: | Chiang Mai University |
| 語言: | English |
| 總結: | 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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