Recommending e-books by multi-layer clustering and locality reconstruction
Haijun Zhang, Shuang Wang, Eric Ke Wang, Yan Li, Yongjun Zhang, Dianhui Chu · 2017
Dramatic growth of e-book sales revenue in recent years makes book recommendations essential to readers. Traditional bag-of-words models have difficulty of capturing the spatial information of terms over books. In this paper, a three-layer tree structure is used for representing each book. A framework, Tree2Vector, is designed for transforming tree-based book data into vectorial space. First, in order to characterize the global discriminative information of child nodes conveyed at the same level of all the trees, a clustering technique is used for assigning child nodes into different clusters, which are adopted for formulating the components of a vector. Furthermore, a locality reconstruction (LR) method is designed to model the reconstruction process, where each parent node is supposed to be reconstructed by its child nodes. The derived reconstruction coefficients are used for locally weighting the components of the vector. The process is repeated level-by-level until a vectorial representation is accomplished for a book tree. Our method is examined in content-based book recommendation. Experimental results exhibit the effectiveness of our framework.