A Comparison of Dimensionality Reduction Algorithms for Improving Efficiency of PromoRank

Abstract

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. For example, when a client borrows a book from a library, the library might want to suggest another related books to them based on their interest. However, since the object, e.g., book, may not be highly ranked in the global property space, PromoRank algorithm promotes a given object by discovering subspaces in which the target is top rank. Nevertheless, the computation complexity of PromoRank is exponential to the dimension of the space. This paper proposes to use dimensionality reduction algorithms, such as PCA or FA, in order to reduce the dimension size and, as a consequence, improve the performance of PromoRank. This paper evaluates multiple dimensionality reduction algorithms to obtain the understanding about the relationship between properties of data sets and algorithms such that an appropriate algorithm can be selected for a particular data set.