A rating prediction method for e-commerce application using ordinal regression based on LDA with multi-modal features

Abstract

This paper presents a new method for rating prediction in e-commerce, which uses ordinal regression based on linear discriminant analysis (LDA) with multi-modal features. In order to realize accurate recommendation in e-commerce, the proposed method estimates each user's rating for target items. Note that we define the rating as “the degree of preference for each item by a user.” For estimating the target user's preference of each item from the past ratings of other items, the proposed method performs training from pairs of “ratings of items” and their feature vectors using ordinal regression based on LDA. Furthermore, in this approach, new features are obtained by applying canonical correlation analysis (CCA) to textual and visual features extracted from review's texts and images on the Web, respectively. Therefore, higher performance of the rating prediction can be realized by our method than that when using single kind of features. Experimental results obtained by applying the proposed method to an actual movie data set, which has been provided by SNAP, show the effectiveness of the proposed method.